chore: bump version to 1.6.9

- The guard snapshotted live threads by `Thread.ident`, but idents are
  recycled after a thread exits — a new leaked thread reusing an exited
  thread's ident would be mistaken for pre-existing and missed (false
  negative). Snapshot the Thread OBJECTS and compare by identity instead.
- Fix the `serve` fixture docstring: the factory returns the ephemeral
  port, not the server.

.github/workflows/ci.yml

@@ -14,8 +14,8 @@ jobs:
   lint:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
+      - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
         with:
           python-version: "3.14"
       - run: pip install pre-commit
@@ -25,8 +25,8 @@ jobs:
   typecheck:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
+      - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
         with:
           python-version: "3.14"
       - run: pip install mypy
@@ -42,8 +42,8 @@ jobs:
       matrix:
         python-version: ["3.11", "3.12", "3.13"]
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
+      - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
         with:
           python-version: ${{ matrix.python-version }}
       # Node is required by tests/test_renderer_js.py — without
@@ -82,8 +82,8 @@ jobs:
           --health-timeout=5s
           --health-retries=5
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
+      - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
         with:
           python-version: "3.14"
       - uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6
@@ -97,8 +97,8 @@ jobs:
   wheel-completeness:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
+      - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
         with:
           python-version: "3.14"
       - run: pip install build
@@ -113,16 +113,9 @@ jobs:
             | grep -v '\.py$' | grep -v '\.dist-info' | grep -v '\.pyc' | grep -v '^File$' \
             | sort)
 
-          # Files intentionally excluded from the wheel (one per line).
-          # The vllm-litellm/ deploy example ships in the repo, not the wheel
-          # (you clone the repo to run it; the package doesn't reference it).
+          # Files intentionally excluded from the wheel (one per line)
           ALLOW="
           turnstone/core/storage/migrations/script.py.mako
-          turnstone/deploy/vllm-litellm/.env.example
-          turnstone/deploy/vllm-litellm/README.md
-          turnstone/deploy/vllm-litellm/docker-compose.yml
-          turnstone/deploy/vllm-litellm/gemma.Dockerfile
-          turnstone/deploy/vllm-litellm/litellm-config.yaml
           "
 
           MISSING=$(comm -23 <(echo "$SOURCE") <(echo "$WHEEL") \
@@ -146,12 +139,12 @@ jobs:
           /tmp/smoke/bin/turnstone-console --help
           /tmp/smoke/bin/turnstone-admin --help
           /tmp/smoke/bin/turnstone-channel --help
-          /tmp/smoke/bin/turnstone-doctor --help
+          /tmp/smoke/bin/turnstone-bootstrap --help
 
   lock-check:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
       - uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
         with:
           uv-version: "0.9.18"
@@ -160,11 +153,11 @@ jobs:
   security:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
       - uses: astral-sh/setup-uv@fac544c07dec837d0ccb6301d7b5580bf5edae39 # v8.2.0
         with:
           uv-version: "0.9.18"
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
+      - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
         with:
           python-version: "3.14"
       - run: uv sync --frozen --all-extras
@@ -188,7 +181,7 @@ jobs:
       run:
         working-directory: sdk/typescript
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
       - uses: actions/setup-node@48b55a011bda9f5d6aeb4c2d9c7362e8dae4041e # v6
         with:
           node-version: "24"

.github/workflows/claude-code-review.yml

@@ -1,46 +0,0 @@
-name: Claude Code Review
-
-on:
-  pull_request:
-    types: [opened, synchronize, ready_for_review, reopened]
-    # Optional: Only run on specific file changes
-    # paths:
-    #   - "src/**/*.ts"
-    #   - "src/**/*.tsx"
-    #   - "src/**/*.js"
-    #   - "src/**/*.jsx"
-
-jobs:
-  claude-review:
-    if: github.event.pull_request.head.repo.full_name == github.repository
-    # Optional: Filter by PR author
-    # if: |
-    #   github.event.pull_request.user.login == 'external-contributor' ||
-    #   github.event.pull_request.user.login == 'new-developer' ||
-    #   github.event.pull_request.author_association == 'FIRST_TIME_CONTRIBUTOR'
-
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      pull-requests: write # post the review + inline comments
-      issues: read
-      id-token: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
-        with:
-          fetch-depth: 1
-
-      - name: Run Claude Code Review
-        id: claude-review
-        uses: anthropics/claude-code-action@01872ccc02bf66740207fb338a783ce028216758 # v1
-        with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-          allowed_bots: 'renovate[bot]' # let Renovate PRs get reviewed
-          plugin_marketplaces: 'https://github.com/anthropics/claude-code.git'
-          plugins: 'code-review@claude-code-plugins'
-          prompt: '/code-review:code-review ${{ github.repository }}/pull/${{ github.event.pull_request.number }}'
-          # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
-          # or https://code.claude.com/docs/en/cli-reference for available options
-

.github/workflows/claude.yml

@@ -1,63 +0,0 @@
-name: Claude Code
-
-on:
-  issue_comment:
-    types: [created]
-  pull_request_review_comment:
-    types: [created]
-  issues:
-    types: [opened, assigned]
-  pull_request_review:
-    types: [submitted]
-
-jobs:
-  claude:
-    if: |
-      (
-        github.event_name == 'issue_comment' &&
-        contains(github.event.comment.body, '@claude') &&
-        contains(fromJSON('["OWNER","MEMBER","COLLABORATOR"]'), github.event.comment.author_association)
-      ) || (
-        github.event_name == 'pull_request_review_comment' &&
-        contains(github.event.comment.body, '@claude') &&
-        contains(fromJSON('["OWNER","MEMBER","COLLABORATOR"]'), github.event.comment.author_association)
-      ) || (
-        github.event_name == 'pull_request_review' &&
-        contains(github.event.review.body, '@claude') &&
-        contains(fromJSON('["OWNER","MEMBER","COLLABORATOR"]'), github.event.review.author_association)
-      ) || (
-        github.event_name == 'issues' &&
-        (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')) &&
-        contains(fromJSON('["OWNER","MEMBER","COLLABORATOR"]'), github.event.issue.author_association)
-      )
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      pull-requests: write # post comments/reviews when @-mentioned on a PR
-      issues: write # post comments when @-mentioned on an issue
-      id-token: write
-      actions: read # Required for Claude to read CI results on PRs
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
-        with:
-          fetch-depth: 1
-
-      - name: Run Claude Code
-        id: claude
-        uses: anthropics/claude-code-action@01872ccc02bf66740207fb338a783ce028216758 # v1
-        with:
-          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
-
-          # This is an optional setting that allows Claude to read CI results on PRs
-          additional_permissions: |
-            actions: read
-
-          # Optional: Give a custom prompt to Claude. If this is not specified, Claude will perform the instructions specified in the comment that tagged it.
-          # prompt: 'Update the pull request description to include a summary of changes.'
-
-          # Optional: Add claude_args to customize behavior and configuration
-          # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
-          # or https://code.claude.com/docs/en/cli-reference for available options
-          # claude_args: '--allowed-tools Bash(gh pr *)'
-

.github/workflows/docker-publish.yml

@@ -7,9 +7,7 @@ on:
 
 concurrency:
   group: docker-${{ github.event.workflow_run.head_sha }}
-  # Never cancel mid-push: an interrupted multi-tag push can leave the
-  # registry with a partial tag set (e.g. :latest moved, :stable not).
-  cancel-in-progress: false
+  cancel-in-progress: true
 
 permissions:
   contents: read
@@ -21,24 +19,15 @@ env:
 
 jobs:
   docker:
-    # Same gate as publish.yml: workflow_run fires for every CI completion
-    # (including fork and same-repo PR runs) with this repo's token and
-    # packages:write.  Only same-repo tag pushes may publish images; CI's
-    # push trigger matches main/stable/* and v* tags, so a head_branch
-    # starting with "v" is necessarily a tag run.
     if: >-
       github.event.workflow_run.conclusion == 'success' &&
-      github.event.workflow_run.event == 'push' &&
-      github.event.workflow_run.head_repository.full_name == github.repository &&
-      startsWith(github.event.workflow_run.head_branch, 'v')
+      github.event.workflow_run.head_repository.full_name == github.repository
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with:
           ref: ${{ github.event.workflow_run.head_sha }}
           fetch-depth: 0
-          # The docker build only reads the tree; keep the token out of it.
-          persist-credentials: false
 
       - name: Resolve release tag
         id: tag
@@ -54,7 +43,7 @@ jobs:
 
       - name: Log in to GHCR
         if: steps.tag.outputs.skip == 'false'
-        uses: docker/login-action@af1e73f918a031802d376d3c8bbc3fe56130a9b0 # v4
+        uses: docker/login-action@650006c6eb7dba73a995cc03b0b2d7f5ca915bee # v4
         with:
           registry: ${{ env.REGISTRY }}
           username: ${{ github.actor }}
@@ -78,12 +67,12 @@ jobs:
           fi
           echo "tags=${TAGS}" >> "$GITHUB_OUTPUT"
 
-      - uses: docker/setup-buildx-action@bb05f3f5519dd87d3ba754cc423b652a5edd6d2c # v4
+      - uses: docker/setup-buildx-action@d7f5e7f509e45cec5c76c4d5afdd7de93d0b3df5 # v4
         if: steps.tag.outputs.skip == 'false'
 
       - name: Build and push
         if: steps.tag.outputs.skip == 'false'
-        uses: docker/build-push-action@53b7df96c91f9c12dcc8a07bcb9ccacbed38856a # v7
+        uses: docker/build-push-action@f9f3042f7e2789586610d6e8b85c8f03e5195baf # v7
         with:
           context: .
           push: true

.github/workflows/publish.yml

@@ -7,9 +7,7 @@ on:
 
 concurrency:
   group: publish-${{ github.event.workflow_run.head_sha }}
-  # Never cancel a publish mid-upload: a half-uploaded release (sdist up,
-  # wheel missing) cannot be re-run cleanly because PyPI rejects duplicates.
-  cancel-in-progress: false
+  cancel-in-progress: true
 
 permissions:
   contents: write
@@ -17,26 +15,14 @@ permissions:
 
 jobs:
   publish:
-    # workflow_run fires for EVERY CI completion — including CI runs for
-    # pull_requests from forks — and always executes here with this repo's
-    # secrets, tokens, and the pypi environment.  Gate to same-repo tag
-    # pushes only: CI's push trigger matches branches main/stable/* and
-    # tags v*, so a head_branch starting with "v" is necessarily a tag run.
-    if: >-
-      github.event.workflow_run.conclusion == 'success' &&
-      github.event.workflow_run.event == 'push' &&
-      github.event.workflow_run.head_repository.full_name == github.repository &&
-      startsWith(github.event.workflow_run.head_branch, 'v')
+    if: github.event.workflow_run.conclusion == 'success'
     runs-on: ubuntu-latest
     environment: pypi
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with:
           ref: ${{ github.event.workflow_run.head_sha }}
           fetch-depth: 0
-          # python -m build executes the tree's build backend; don't leave
-          # the contents:write token sitting in .git/config while it runs.
-          persist-credentials: false
 
       - name: Resolve release tag
         id: tag
@@ -50,7 +36,7 @@ jobs:
             echo "skip=false" >> "$GITHUB_OUTPUT"
           fi
 
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
+      - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
         if: steps.tag.outputs.skip == 'false'
         with:
           python-version: "3.14"
@@ -63,7 +49,7 @@ jobs:
 
       - name: Create GitHub Release
         if: steps.tag.outputs.skip == 'false'
-        uses: softprops/action-gh-release@718ea10b132b3b2eba29c1007bb80653f286566b # v3
+        uses: softprops/action-gh-release@b4309332981a82ec1c5618f44dd2e27cc8bfbfda # v3
         with:
           tag_name: ${{ steps.tag.outputs.tag }}
           generate_release_notes: true

.github/workflows/understone-example.yml

@@ -31,8 +31,8 @@ jobs:
         # Floor and ceiling of the example's requires-python (>=3.11).
         python-version: ["3.11", "3.13"]
     steps:
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
-      - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
+      - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6
         with:
           python-version: ${{ matrix.python-version }}
       - run: pip install -e ".[test,dev]"

.github/workflows/vendor-js.yml

@@ -25,43 +25,22 @@ permissions:
 
 jobs:
   vendor-js:
-    # Same-repo PRs only: this job checks out the PR head and pushes to it
-    # with contents:write, so it must never act on a fork's branch.
-    # Gate on the PR author (immutable), not github.actor (names whoever
-    # caused the latest event, which can be someone else re-running it).
-    if: >-
-      (github.event_name == 'pull_request' &&
-       github.event.pull_request.user.login == 'renovate[bot]' &&
-       github.event.pull_request.head.repo.full_name == github.repository) ||
-      github.event_name == 'workflow_dispatch'
+    if: github.actor == 'renovate[bot]' || github.event_name == 'workflow_dispatch'
     runs-on: ubuntu-latest
     steps:
       - name: Resolve PR head ref
         id: ref
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          # Branch names may contain shell metacharacters; pass via env,
-          # never interpolate ${{ }} into the script body.
-          HEAD_REF: ${{ github.head_ref }}
-          PR_NUMBER: ${{ inputs.pr_number }}
         run: |
-          if [[ "$GITHUB_EVENT_NAME" == "workflow_dispatch" ]]; then
-            # The dispatch input is an arbitrary PR number; refuse fork PRs.
-            # A fork's headRefName is a bare branch name that may collide
-            # with a branch in this repo, and checkout+push would then hit
-            # that unrelated branch ("same-repo PRs only" applies here too).
-            pr_json=$(gh pr view "$PR_NUMBER" --repo "$GITHUB_REPOSITORY" --json headRefName,isCrossRepository)
-            if [[ "$(jq -r '.isCrossRepository' <<< "$pr_json")" != "false" ]]; then
-              echo "::error::PR #${PR_NUMBER} head is not a branch in this repository; refusing to complete it."
-              exit 1
-            fi
-            ref=$(jq -r '.headRefName' <<< "$pr_json")
+          if [[ "${{ github.event_name }}" == "workflow_dispatch" ]]; then
+            ref=$(gh pr view "${{ inputs.pr_number }}" --repo "${{ github.repository }}" --json headRefName -q .headRefName)
           else
-            ref="$HEAD_REF"
+            ref="${{ github.head_ref }}"
           fi
           echo "head_ref=${ref}" >> "$GITHUB_OUTPUT"
 
-      - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7
+      - uses: actions/checkout@df4cb1c069e1874edd31b4311f1884172cec0e10 # v6
         with:
           ref: ${{ steps.ref.outputs.head_ref }}
 

.gitignore

@@ -28,4 +28,3 @@ tools/skill_audit_analysis/data/
 tools/skill_audit_analysis/output/
 design_ideas/
 .claude/
-docs/design/

CHANGELOG.md

@@ -13,165 +13,6 @@ stable, and the experimental line:
 - **`stable/1.6`** — patch-only (`v1.6.x`)
 - **`main`** — experimental (next major)
 
-## [1.7.0]
-
-The headline of the 1.7 line is **Personas** — operator-authored control
-over how each workstream composes its system message and capability
-envelope. The rest of the release hardens the pieces a persona leans on:
-concurrent approvals, cross-provider reasoning-effort control, cooperative
-compaction, multi-user session safety, and MCP resilience for unattended
-work.
-
-> **⚠️ Before upgrading:** 1.7.0 adds Alembic migrations `062`–`065`,
-> applied automatically on first start (projects, personas, and two
-> smaller schema tidy-ups). Migration `063` creates the `personas` table
-> with its six seed personas and converts existing `creative_mode`
-> workstreams to the `writer` persona in place. The changes are additive
-> to your conversation data, but — as always — back up your storage before
-> upgrading (`pg_dump` for PostgreSQL; copy the database file for SQLite).
-
-**Breaking changes at a glance** (details in the sections below): the
-`/creative` REPL toggle removed (replaced by the `writer` persona), the
-`turnstone-bootstrap` entry point renamed to `turnstone-doctor`, and the
-approval-status API/SDK field `pending_approval_details` changed from a
-single object to a list (one entry per concurrent approval cycle).
-
-### Added
-
-- **Personas** (#683) — a named, reusable bundle attached to a workstream
-  at creation, controlling system-message composition and the capability
-  envelope via exactly four levers: base-prompt override, tool visibility
-  set, MCP on/off, and memory on/off. The persona is resolved once and
-  snapshotted into `workstream_config`; editing or archiving a persona
-  never changes an existing workstream. Six seed personas ship with
-  migration `063` (`engineer` and `orchestrator` are the per-kind
-  defaults with no overrides, so zero-touch behavior is unchanged;
-  `scribe`, `researcher`, `writer`, and `executive` are curated
-  envelopes). Selectable on every creation surface (web pickers, the
-  create API/SDKs, coordinator `spawn_workstream` / `spawn_batch`, and
-  `turnstone --persona <name>`); authored in the console's new
-  Governance → Personas tab (`persona.{create,read,write}` perms,
-  archive-only lifecycle). See `docs/personas.md`.
-- **Projects — governed resource containers** (#724) — group workstreams
-  and their resources under a project (migration `062`), with
-  project-scoped memory, a per-project resources view, a project column on
-  the saved list, and server-enforced private-project workstream
-  visibility.
-- **Task-agent sub-harness** (#732) — a spawned task agent now runs on its
-  own Turn-IR sub-harness with parent-tagged step events: its sub-tool
-  steps nest inside an expandable card in the parent trajectory, its
-  sub-trajectory is recallable, and each agent gets read isolation from
-  its siblings.
-- **MCP static-server autonomous reconnect** (#768) — statically
-  configured MCP servers are now kept live by a health loop
-  (capped-jittered backoff, ping-based liveness) instead of silently
-  staying dead after the first transport drop.
-- **Attachments — capability-gated client-side fallback** — when the
-  active model can't natively handle an attachment, the client degrades
-  gracefully (PDF → extracted text, audio → transcript) instead of
-  failing the turn.
-- **Eval measurement / optimizer split** (#763, #765) — `turnstone-eval`
-  is now a measure-only substrate with the prompt optimizer factored out,
-  plus a new skill-adherence measurement mode.
-- **Deployment examples** — a vLLM + LiteLLM unified-memory inference
-  example showing a 3-model co-resident stack with an HF loader (#686,
-  #688), and an Altair + `vl-convert-python` visualization stack (#685).
-- **Concurrent approvals and a long-session frontend overhaul** (#754,
-  #755, #773, #775) — the live-session frontend was reworked for long
-  runs (the pipeline is wedge-proofed and its hot paths de-O(N)'d), and on
-  top of it a workstream can now hold more than one tool call awaiting
-  approval at a time. Each parallel batch gets its own approval cycle,
-  with one card per pending call in the interactive and coordinator UIs,
-  cycle-keyed tracking in Slack and Discord, and cycle-routed resolution
-  across the server/console/SDK APIs; sub-agent tool gates run the
-  intent-judge pipeline as their own generation. The send button no longer
-  sticks disabled after a batch resolves — orphaned approval cycles are
-  pruned and the app is the sole owner of the button state.
-  *(BREAKING: the `pending_approval_details` field is now a list, oldest
-  first.)*
-- **Reasoning-effort control on every provider lane** (#771, #774) — the
-  session effort knob now reaches local backends too: it drives
-  `chat_template_kwargs` on the anthropic-compatible and openai-compatible
-  lanes and threads through to Gemini and xAI, alongside the commercial
-  providers that handle effort natively. The console surfaces each model's
-  effective effort ladder in plain words and adds an always-on
-  thinking-mode option to the model form. Effort snapping is ordinal —
-  it rounds up and caps at the model's ceiling rather than silently
-  dropping.
-
-### Changed
-
-- **Skills are capability-context, not identity** (#762) — a task agent's
-  identity now comes from its persona; an applied skill's body is demoted
-  to capability context and moved out of the identity system message.
-  Skill-body substitution is unified across every invocation context so
-  the same skill renders identically whether loaded interactively, by the
-  model, or inside a sub-agent.
-- **`turnstone-doctor` replaces `turnstone-bootstrap`** (#718)
-  *(BREAKING)* — the setup/diagnostics entry point is renamed; update any
-  scripts or service units that invoke `turnstone-bootstrap`.
-- **Honest cancellation dispositions** — cancelled or timed-out
-  side-effecting tools now report an `UNKNOWN` disposition rather than a
-  flat failure, tool dispositions are typed (not just prose), and a
-  coordinator cancel propagates down the sub-tree.
-- **Multi-user shared-workstream context** (#750) — in a shared
-  workstream, send is gated to the acting participant while a turn is in
-  flight (both the interactive and coordinator surfaces), cross-user
-  mid-turn interjections are blocked, and shared-workstream state plus
-  fork sender attribution are now durable.
-- **Cooperative compaction** (#730) — the context budget is anchored to
-  the provider's true capacity, the summary call is chunked so it can't
-  overflow, and the active plan and the outstanding ask are carried across
-  compaction verbatim. The `recall` tool is scoped to the compacted-away
-  past.
-- **Intent judge sees the full tool arguments** (#760) — the judge's
-  argument projection is no longer narrowed, so it stops issuing confident
-  false denials on a partial view. The output-guard judge sources its real
-  context window, and `context_window = 0` in `config.toml` now means
-  auto-detect.
-
-### Fixed
-
-- **Compaction resume hardening** (#731) — checkpoint markers are
-  persisted so resume rehydration is bounded, context-overflow on resume
-  is recovered across providers, and a recognized rate-limit is no longer
-  misclassified as context overflow.
-- **MCP unattended-work resilience** (#706, #742, #767) — dead-transport
-  handling is completed, consented OAuth (OBO) tokens are refreshed
-  proactively so autonomous runs don't strand on an expired grant, the
-  Entra ID on-behalf-of impersonation flow blockers are closed (migration
-  `065` adds the OIDC `oid`), and OAuth refresh failures are classified so
-  a transient blip never revokes consent nor a dead grant strands the
-  user.
-- **Memory writes** (#735) — save/update is a single atomic upsert, and
-  writing a memory no longer recomposes the system prefix mid-session.
-
-### Removed
-
-- **`/creative` removed** *(BREAKING)* — subsumed by the Personas feature
-  above: the REPL toggle (and its tab completion) is gone, and the
-  `writer` seed persona replaces it — start a session with
-  `turnstone --persona writer` or pick *Writer* in the web
-  pickers. Unlike the old fork, the writer persona composes the full
-  system message, so session context and mandatory prompt policies now
-  apply to prose-only sessions too. The `creative_mode` key in
-  `workstream_config` is no longer read or written. Migration `063`
-  converts existing creative-mode workstreams to the `writer` persona
-  automatically, so they resume as writing sessions rather than as
-  legacy defaults.
-
-### Security
-
-- **High-risk skill activation is gated** (#762) — a model-initiated load
-  of a `high`- or `critical`-risk skill is gated and fails closed when the
-  backing storage is unavailable, so an untrusted turn can't silently
-  pull in a dangerous capability.
-- **Dependency security floors** — `cryptography` and `starlette` are
-  pinned to security-fixed minimums.
-- **CI publish hardening** — the vendored-JS dispatch path refuses fork
-  PRs, and `workflow_run` publishing is gated to same-repo tag pushes, so
-  a fork can't trigger a release build.
-
 ## [1.6.0]
 
 The first stable release of the 1.6 line — and the first under Apache 2.0.

Dockerfile

@@ -8,7 +8,7 @@ FROM python:3.14-slim
 LABEL org.opencontainers.image.title="turnstone" \
       org.opencontainers.image.description="Multi-node AI orchestration platform"
 
-COPY --from=ghcr.io/astral-sh/uv:0.11.26 /uv /usr/local/bin/uv
+COPY --from=ghcr.io/astral-sh/uv:0.11.21 /uv /usr/local/bin/uv
 
 # Remove the slim image's man page exclusion so man-db has actual content
 RUN rm -f /etc/dpkg/dpkg.cfg.d/docker

HYPOTHESIS.md

@@ -1,207 +0,0 @@
-# What is a harness?
-
-*A hypothesis — not a theorem. The honest answer is a claim about **shape**: an object you can write down that says what a harness is and, just as precisely, the guarantee it cannot carry for free.*
-
-Most descriptions of an agent framework are a feature list. This is an attempt at a definition.
-
----
-
-## The claim
-
-*Informal.* A harness is a **stopped, deterministically-controlled Markov process on task-state, closed around a stopped autoregressive process on context-space, driven by a learned model kernel** — a deterministic controller in closed loop with a stochastic learned plant.
-
-*In plain terms.* The **harness** is the whole governed loop: a deterministic **shell** you write — build the prompt, authorize an action, fold the response back into state — wrapped around a black-box stochastic model kernel (the **plant**, $M_W$) and the environment its actions touch, looped until it halts in $H$. The shell is deterministic, $M_W$ is not, and everything below makes that split precise.
-
-*Formal — the objects.* A harness is a tuple $\mathcal{H} = (\mathcal{S}, \mathcal{C}, \mathcal{Y}, \mathcal{A}, \mathcal{E}, \pi, M_W, \gamma, Q_E, \rho, H, H_{\mathrm{ok}}, B)$ over **standard Borel** spaces (concretely: the *controlled* state is standard Borel by construction — token sequences, finite config maps, bounded counters and ledgers, finite tuples of real vectors — and the model/environment coordinates are inherited as such whenever they serialize to a Polish space; the assumption is roomier than it looks — even a belief-state coordinate valued in $\mathcal{P}(X)$ survives, since $\mathcal{P}(X)$ is Polish for Polish $X$ — and fails only for a genuinely non-separable coordinate, an uncountable product $\sigma$-algebra being the canonical hazard, which this construction avoids): a deterministic lowering $\pi:\mathcal{S}\to\mathcal{C}$; a stochastic model-run kernel $M_W(c, dy)$ into a readout space $\mathcal{Y}$ (which includes the parse-failure $\bot$, so $M_W$ and $\gamma$ are total over it); a deterministic **authorization gate** $\gamma:\mathcal{S}\times\mathcal{Y}\to\mathcal{A}_{\bot}$ that validates the model's parsed readout into an authorized action in $\mathcal{A}$ or rejects it as $\bot$ (parsing itself lives inside $M_W$ — realized as the readout $R$ of the specialization below); a stochastic environment/tool kernel $Q_E:\mathcal{S}\times\mathcal{A}_{\bot}\rightsquigarrow\mathcal{E}$ on the authorized action (rejection included, with $Q_E(s,\bot,\cdot)=\delta_{e_0}$ for a distinguished no-op response $e_0\in\mathcal{E}$); and a deterministic verify-and-fold-back map $\rho:\mathcal{S}\times\mathcal{Y}\times\mathcal{A}_{\bot}\times\mathcal{E}\to\mathcal{S}$.
-
-*Terminal structure.* The terminal set is an absorbing halt set $H\subseteq\mathcal{S}$ (the daemon "ready-state" recurrence of the note below is a separate, non-absorbing object) with accepting subset $H_{\mathrm{ok}}\subseteq H$; separately, a bad set $B\subseteq\mathcal{S}$ ($B\cap H_{\mathrm{ok}}=\varnothing$) marks the unsafe states for reach-avoid, possibly entered before any halt; hitting times are $\tau_A=\inf\{n\ge 0:s_n\in A\}$, and $\tau_H$ is a stopping time for the natural filtration.
-
-*The outer kernel.* The induced outer transition kernel, for $s\notin H$, is
-
-$$T(s, A) = \int_{\mathcal{Y}}\!\int_{\mathcal{E}} \mathbf{1}_A\!\big(\rho(s, y, \gamma(s,y), e)\big)\; Q_E\big(s, \gamma(s,y), de\big)\; M_W(\pi(s), dy), \qquad T(s,A)=\mathbf{1}_A(s)\ \text{ for } s\in H,$$
-
-and the harness runs $s_{n+1} \sim T(s_n)$ from an initial $s_0 \sim \mu_0$ until $\tau_H = \inf\{n : s_n \in H\}$. Because $\pi, \gamma, \rho, H$ are deterministic they contribute no integration variable of their own — they appear as measurable transformations inside the integrand (the pushforward), not literally outside it — so the controller injects no randomness, and every coin is inherited from $M_W$ and $Q_E$. (The earlier shorthand $T = \rho \circ (M_W \circ \pi, E)$ is suggestive but ill-typed — $M_W$ returns a *law*, while $\rho$ consumes a *sample* together with the prior state $s$; the integral is what the shorthand meant.)
-
-*Fail-closed.* The gate $\gamma$ is what makes **fail-closed** a property, not just a name: model output is an *untrusted proposal*, and $\gamma(s,y)=\bot$ forces a no-op environment response ($Q_E(s,\bot,\cdot)=\delta_{e_0}$) — so a malformed or unauthorized tool call is rejected *before* it can act, not validated after its side effects have landed. Fail-closed is then the property that a rejected proposal causes *no unauthorized side effect* and lands in a **safe, non-bad** set ($\rho(s,y,\bot,e_0)\notin B$): a non-accepting terminal $H\setminus H_{\mathrm{ok}}$ in the strict case, or a safe non-terminal state when the spec retries. And $\rho$ must validate the tool *response* $e$, not only the proposal that $\gamma$ already gated: a malformed or adversarial response $e$ is caught at fold-back, not just at the gate. But response-validation has a hard limit: $\rho$ can reject a bad tool *response*, yet it cannot undo side effects an *authorized* action already caused — so $\gamma$, not $\rho$, is the last line before irreversible effects, and anything irreversible must be gated at authorization. The boundary is also only real if raw model output reaches *no* sink — tool, logger, browser, or remote call — before $\gamma$; any pre-authorization escape bypasses the gate. The user-visible final response and any logging are themselves effects, and the rule binds *model-authored* bytes: they reach a sink either as an authorized action through $\gamma$, or only after an accepted halt in $H_{\mathrm{ok}}$. Shell-*templated* text — a refusal notice, a cancellation report reading the ledger — is controller output, outside $\gamma$'s jurisdiction, and may accompany any halt (a template that *interpolates* model-authored fragments inherits the model's label — the appendix's meet rule — and those bytes are gated like any others); the invariant is that raw model text never reaches a sink ungated, not that failed runs die silent.
-
-*The harness invariants.* These are the invariants that make $\mathcal{H}$ a *harness* and not merely a controlled Markov process with a learned kernel inside: the model sees only $\mathcal{C}$, never full $\mathcal{S}$; its outputs are proposals, not actions; a deterministic capability boundary $\gamma$ gates every side effect; and the *terminal* set $H$ splits into accepting ($H_{\mathrm{ok}}$) and non-accepting ($H\setminus H_{\mathrm{ok}}$ — safe refusals outside $B$, and wrong or bad halts possibly in $B$), while the bad set $B$ is a *separate* unsafe set — possibly absorbing, possibly entered mid-run before any halt — against which $\tau_B$ is measured for reach-avoid. Two notes keep the invariants honest. They are *signature*, not strength: a $\gamma$ that authorizes everything still satisfies the tuple, as a trivial group satisfies the group axioms — the definition admits degenerate harnesses, and fail-closed, provenance isolation, and the certificates below are properties a particular harness *earns*, not gifts of the signature. And the first invariant has a sharper, two-sided form: $\pi$ is the *only* channel from state to model — the confidentiality floor lives at what $\pi$ must never lower (credentials, other principals' data) — exactly as $\gamma$ is the only channel from model output to effect, where the injection bounds live; exfiltration is therefore cut at either chokepoint, never lowered or never emitted (the gate refusing the read whose URL is the payload is the emission-side cut). One chokepoint out of the state, one into the world; a bypass of either is the same bug with the sign flipped.
-
-*Beyond the stationary kernel.* This displayed $T$ is the time-homogeneous, fixed-kernel case; for nonstationary or adversarial environments, replace $Q_E$ with a time-indexed kernel $Q_{E,n}$ — or an admissible family of kernels, or an adversary's policy — over which the robust certificate (the minimax form under *The limit*) quantifies. If that adversary conditions on history rather than only the current $(s, y)$, the history must itself live in $s$ — otherwise the object is a Markov *game* requiring further augmentation, not a Markov chain. And nonstationarity is not the environment's monopoly: a provider retraining or re-serving under a fixed endpoint name is a nonstationary $M_{W,n}$ — the table places model version *in* $s$ precisely so a version bump is a visible state change — and any measured surrogate (the $\delta$ of *The limit*) is calibrated against one kernel and dies with the bump; the dashboard must be keyed to the kernel it measured.
-
-*The inner kernel.* $M_W$ is itself a stopped process, and for a decoder-only transformer it is implemented as
-
-$$M_W(c, \cdot) = \mathrm{Law}\big(R(z_{\tau})\big), \quad z_t = (c_t, b_t, m_t), \quad v \sim K_W(c_t, \cdot), \quad K_W(c, v) = (U \circ \Phi_W \circ \mathrm{Emb})(c)[v], \quad c_{t+1} = \mathrm{suffix}_{\le L}(c_t\!\cdot\! v),\ \ b_{t+1} = b_t\!\cdot\! v,\ \ m_{t+1} = \mathsf{step}(m_t, v),\ \ \tau=\inf\{t:m_t\in\mathrm{Stop}\}.$$
-
-with the layer stack $\Phi_W$ on the residual stream as the (loosely) "manifold" core — formally just the learned high-dimensional residual-stream transformation, with manifold-proper reserved for the frontier. The inner state $z_t=(c_t,b_t,m_t)$ separates the model-visible window $c_t$ (the $\le L$ slice that slides) from the untruncated output buffer $b_t$ (the transcript the readout actually consumes, so truncation never loses it) and the parser/stop state $m_t$ (parser state, a token counter, and a clock, so the cap and timeout are functions of it), updated $m_{t+1}=\mathsf{step}(m_t,v)$, whose stop set $\mathrm{Stop}$ — EOS emitted, max-token cap, timeout, or parse-failure $\bot$ — forces $\tau=\inf\{t:m_t\in\mathrm{Stop}\}$ finite, making $M_W$ a genuine *probability* kernel rather than a sub-probability one completed by a cemetery output. (One honesty note on the clock: a token-count cap is a deterministic function of the run, but a *wall-clock* timeout imports infrastructure noise — server load, batching, congestion — into the kernel's coin; legitimate, a kernel may carry any randomness, but it makes the displayed $M_W$ the model *plus its serving substrate*, and the determinism audit under *How this could be wrong* must hold the clock fixed along with the samples.) The readout is total, $R : \mathcal{Z} \to \mathcal{Y}$ — a parsed tool-call, answer, or transcript, returning the parse-failure $\bot\in\mathcal{Y}$ when parsing fails; crucially $R$ is a *syntactic, verified* readout (parsing and extraction), not a semantic solver, or the $L$-wall below is void — arbitrary computation could hide in $R$ off the $\le L$ window — so $M_W(c, \cdot) = R_{\sharp}\,\mathrm{Law}(z_{\tau})$, the pushforward of the stopped-state law along $R$ (equivalently $M_W(c, A_Y) = \Pr[R(z_{\tau}) \in A_Y \mid z_0 = (c,\varnothing,m_0)]$ for a measurable $A_Y\subseteq\mathcal{Y}$); the no-truncation special case takes $\mathcal{Y}=\mathcal{C}$ with $R(c,b,m)=c$ (the window is the whole transcript), reading $c_{\tau}$ directly. The $\bot$ branch is exactly what $\gamma$ rejects fail-closed. This is a **specialization, not part of the definition**: a harness wrapped around a black-box API is still a harness, and $M_W$ may be any learned kernel. Where the weights are open, the geometry of $\Phi_W$ is where the substrate's continuity lives, and several downstream claims lean on it — but the definition does not.
-
-Two stopped processes, nested: **deterministic control over stochastic dynamics over a learned kernel.** Both loops are hitting-time processes; *some* harnesses additionally read the halt set as a fixpoint or acceptance condition — iterative refinement to self-consistency is the genuine fixpoint case, while EOS, length, and tool-call syntax are not convergence. Neither loop settles because you asked it to. (The clean inner-then-outer nesting assumes tool calls fall *between* model runs; streaming or mid-generation tool calls interleave the two loops and need a finer state machine — the nesting is then an idealization.)
-
-## Reading it
-
-| Symbol | Is |
-|---|---|
-| $\mathcal{H}$ | the harness — the whole controlled system, *not* the model |
-| $s \in \mathcal{S}$ | task-state: IR / dialect stack, tool results, plan, counters, **and every mutable interface variable** (model/tool versions, permissions, retrieved context) — only Markov *after* that augmentation |
-| $\mathcal{C},\ \mathcal{Y},\ \mathcal{A},\ \mathcal{E}$ | the **context / readout / action / effect spaces** — model-visible context $\mathcal{C}$, model readout $\mathcal{Y}$ (incl. the parse-failure $\bot$), authorized actions $\mathcal{A}$ (with $\mathcal{A}_\bot = \mathcal{A}\cup\{\bot\}$), and tool/environment effects $\mathcal{E}$ |
-| $\pi : \mathcal{S} \to \mathcal{C}$ | **lowering** — prompt construction, dialect lowering, effective-program selection (deterministic) |
-| $M_W(c, dy)$ | the **model-run kernel** (inner solver) — a stopped autoregressive process; $\Phi_W$ is the residual-stream ("manifold") core in the transformer case |
-| $Q_E(s, a, de)$ | the **environment/tool kernel** on the authorized action $a\in\mathcal{A}_{\bot}$ (with $Q_E(s,\bot,\cdot)=\delta_{e_0}$, the no-op $e_0$) — tool effects, API responses, the world (possibly adversarial) |
-| $\gamma,\ \rho$ | the deterministic **authorization gate** $\gamma:\mathcal{S}\times\mathcal{Y}\to\mathcal{A}_{\bot}$ (untrusted proposal → authorized action or $\bot$) and the **fail-closed verify-and-fold-back** $\rho:\mathcal{S}\times\mathcal{Y}\times\mathcal{A}_{\bot}\times\mathcal{E}\to\mathcal{S}$ |
-| $H,\ \tau_H$ | the **halt set** (absorbing) and the outer **halting time** — a hitting-time process, not a single pass |
-| $H_{\mathrm{ok}},\ B$ | the **accepting halts** $H_{\mathrm{ok}}\subseteq H$ (correct, successful terminals) and the **bad set** $B$ — unsafe states for reach-avoid ($B\cap H_{\mathrm{ok}}=\varnothing$), *separate* from $H$ and possibly entered mid-run before any halt |
-
-The structural fact that earns the word *controller*: $\pi$, $\gamma$, $\rho$, and the halt test are **deterministic** (and the readout $R$ too, where the transformer specialization is in play), so $\mathcal{H}$ injects no randomness of its own. Every coin is inherited from $M_W$ and $Q_E$. This split — deterministic code around a stochastic oracle — wears two names. In control-theory terms it is **controller vs. plant**: the controller is those deterministic maps; the **plant** is the learned kernel $M_W$, *plant* in its exact sense — the element with its own dynamics you steer but do not author. In engineering terms it is **shell vs. plant**: the **shell** is the entire deterministic outer harness — the control logic *plus* the external memory and tools it administers (the files, databases, vector stores below) — of which the controller is just the control-logic slice. So *shell : plant :: the part you write : the part you don't*; $M_W$ is the only thing on the right, while the environment $Q_E$ is the world the actions meet — a disturbance into the loop, not the plant. (A reader from reinforcement learning or classical control will make the opposite assignment — environment as plant, policy as controller; the inversion is deliberate: in harness engineering the element you are trying to make behave is the model, and the world is what pushes back on the attempt.) This determinism is *conditional* — on versioned code, configuration, model endpoint, and tool interfaces, and on *single-run sequencing*: concurrent runs sharing authorization state re-open a gap the per-run object cannot see (taken up under *Gate placement* in the appendix); any retry, timeout, race, or randomized routing that escapes that conditioning must be modeled explicitly as part of $Q_E$ or the controller, not waved away. The displayed $M_W(c)$ likewise freezes endpoint, version, and sampler; a routing or config change is a state-indexed kernel $M_{\kappa(s)}$ or folds into $K_C$ — the kernel must not silently depend on config the table places in $s$. More generally, control may itself be stochastic — a controller kernel $K_C(s, dc)$ over routing, sampled retries, ensemble votes, learned routers — of which the deterministic $\pi, \gamma, \rho, H$ are the Dirac special case. That case is the one worth wanting: it localizes every coin to $M_W$ and $Q_E$ and keeps the controller/plant split clean. Where control is genuinely stochastic the split does not break, it widens — fold $K_C$ into the kernel and the certificate quantifies over its randomness too. But the guarantees do not soften uniformly, and the component-to-guarantee map is worth stating because it says exactly what may be learned without loss. A learned $\pi$ — retrieval, reranking, summarization inside the lowering — costs only *semantic adequacy*, under one factorization: $\pi$ splits into a deterministic **never-lower filter** — the redaction that keeps credentials and other principals' data out of $\mathcal{C}$ — composed with learned selection, and only the selection may soften, or the confidentiality floor of the invariants note becomes a probability. With the filter Dirac, no-unauthorized-effect is $\gamma$'s property alone, and the reach-avoid certificate survives too, so long as the provenance partition of *The limit* holds. A learned $\gamma$ or $\rho$ costs the thing itself — authorization and ledger integrity are exactly the properties that must stay Dirac, or "no unauthorized effect" and "the ledger is what happened" become probabilities. So the minimal deterministic core is $\{\gamma, \rho, H\}$ plus $\pi$'s never-lower filter: the rest of $\pi$ may soften into a kernel and the harness bends without breaking — fortunate, because every deployed $\pi$ already has learned kernels inside it.
-
-## Why this shape
-
-$$f(x) \;\longrightarrow\; x = f(x;\,W) \;\longrightarrow\; f(x)$$
-
-Classical software, inverted into latent geometry, then re-wrapped in classical software. The harness **re-imposes the determinism the model dissolved**: $\pi, \gamma, \rho$, and the halt test ($H$) are ordinary designed code — a controller — whose primitive operand happens to be a stochastic oracle. That closure is why a compiler is the right mental model (staged deterministic software ports cleanly) and exactly why the analogy breaks (a compiler's primitive operation was never a coin). **The harness is the half you can reason about classically, sitting on top of the half you cannot.**
-
-## The limit, stated honestly
-
-**Raw halting is cheap; correct halting is not.** A **certificate** is a *witness*: a checkable object — here a Lyapunov/drift function $V \ge 0$ — that *provably* satisfies a condition entailing the guarantee, through a standard supermartingale / optional-stopping theorem (the target picks the condition: drift toward $H$ for halting, a barrier for safety, reach-avoid for success). It is not the property, only an object cheap to check and hard to produce. One word then carries two senses, and the seam between them is what this section is about: the **proven** certificate, a $V$ whose bound actually holds; and the **measured** surrogate you fall back on when the architecture exhibits none — a candidate $\hat V$ with a sampled slack $\delta$, a *calibrated risk metric, not a certificate* until that bound is proven (or held to a high-confidence worst case). The gap between the two is the whole honest-limit argument. A deterministic budget — augment $s$ with a counter $k$ decremented each outer step, halting at $k=0$ — makes $V(s)=k$ a trivial Lyapunov certificate for *halting*, so the architecture does not lack a halting guarantee by construction. What it lacks for free is a certificate of *correct, safe, successful* halting under the learned dynamics. The un-budgeted halting object is still worth stating, since it shows where even the easy guarantee comes from: a certificate would be *sufficient* for almost-sure halting with bounded expected runtime — a $V \ge 0$ with
-
-$$\mathbb{E}[\,V(s_{n+1}) \mid s_n\,] \le V(s_n) - \varepsilon \quad\text{off the halt set}$$
-
-bounds $\mathbb{E}[\tau_H] \le V(s_0)/\varepsilon$ under the usual integrability and optional-stopping conditions. Nothing in the harness hands you such a $V$ the way a compiler's structure does: a specific compiler analysis gets its $V$ for free where a finite-height lattice *is* a well-founded descent — termination by construction *for that analysis*, not for a whole compiler — and the harness has no analogous built-in descent for its model/environment loop.
-
-But the relevant $V$ is not *absent* — and this is the subtlety the blunt phrasing erased. The minimal certificate exists and is **forced**: it is the expected halting time itself,
-
-$$V^\star(s) = \mathbb{E}[\,\tau_H \mid s_0 = s\,],$$
-
-finite wherever $H$ is reached in finite expected time — the domain $\{s : \mathbb{E}_s[\tau_H] < \infty\}$ — though note this $V^\star$ certifies *halting* (reaching the terminal set $H$ at all), not *correct* halting; the stronger object, the expected time to an accepting $H_{\mathrm{ok}} \subseteq H$, is $V^\star_{\mathrm{ok}}$, taken up at the second wall below. So the honest claim splits in two: the architecture provides no certificate *for free*, and the one that exists is — **conjecturally, not as a theorem** — a functional of $W$ and the environment that does not compress below model scale. The conjecture needs scoping, because the per-step drift splits by coordinate (made precise below) and the shell's contribution is an exact, designed descent of low description complexity *by construction* — so whatever is incompressible is not the shell's part but the **plant's**, the contribution $M_W$ supplies. And even there it is conjecture with a live counter-possibility, not foregone hardness: $V^\star$ is a *coarse* functional — one scalar, an expected hitting time, not the full output law — and coarse functionals of complicated kernels are sometimes cheap (absorbing chains with sparse transition structure have tractable expected hitting times over enormous state spaces). So the honest form is conditional: *if* the plant's contribution to the drift admits no certificate of description length materially below $|W|$, then ours is as hard as the dynamics — but that antecedent is the unproven part, and the flat phrasing of an earlier draft ("the dynamics it certifies *are* the weights") overstated it by treating a coarse hitting-time functional as if it carried the whole distribution. The compiler's certificate is structurally trivial; ours is *plausibly* as hard as the plant dynamics, though whether useful compressed certificates exist — for the coarse hitting-time functional, or for structured sub-tasks — is open. This is the quantitative form of *you can borrow how LLVM is built — not, in general, why it is correct.*
-
-So you never compute $V^\star$. You pick a candidate $\hat V$ and **estimate its drift slack**
-
-$$\delta = \sup_{s \notin H}\Big(\mathbb{E}[\,\hat V(s_{1}) \mid s_0 = s\,] - \hat V(s) + \varepsilon\Big).$$
-
-The status of $\delta$ has to be stated carefully, because it is easy to oversell. If you can establish a *high-confidence upper bound* on the true worst-case slack and it is $\le 0$, optional stopping hands you a real, conservative certificate, $\mathbb{E}[\tau_H] \le \hat V(s_0)/\varepsilon$. But an *empirical* $\delta$ estimated from sampled states is **not** a certificate: a measured $\delta > 0$ may mean the candidate $\hat V$ is poor, the sampled distribution missed rare failures, the supremum was never attained in-sample, the process is non-stationary, or the state abstraction is not Markov. So $\delta$ is **the number on the dashboard** — a *calibrated risk metric*, the evaluable surrogate for a guarantee the geometry will not give you, and a genuine bound only once it is statistically controlled against rare-event and adversarial tests. A weaker result is still useful: a true bound $\delta \le \bar\delta < \varepsilon$ (rather than $\le 0$) leaves descent intact with effective slack $\varepsilon - \bar\delta$ and $\mathbb{E}_s[\tau_H] \le \hat V(s)/(\varepsilon - \bar\delta)$. And the empirical quantity is distributional, not a supremum — write $\delta_{\nu}$ for drift averaged over a sampled $\nu$, reserving $\delta_{\sup}$ for the worst-case bound; only $\delta_{\sup}$ certifies. Its empirical noise floor and residual risk are driven by the measure $\mu(D)$ of the divergent region $D=\{s:\mathbb{E}_s[\tau_H]=\infty\}$ (states from which $H$ is not reached in finite expected time, under the reference/sampling measure $\mu$), the coverage of the sampled state distribution, and the hitting-time variance $\mathrm{Var}[\tau_H]$ — properties of the trained weights, the environment, and the evaluation distribution, knowable only a posteriori.
-
-> For an agent *meant* to run forever — a coordinator, a daemon — halting is the wrong target, and $V^\star = \infty$ is the spec, not a pathology. The same drift theory then certifies **recurrence to a ready-state** instead of absorption to a halt-set. The object changes; the missing certificate does not. Safety changes shape too: it is no longer the one-shot $\Pr_s(\tau_B=\infty)$ but a *per-cycle* hazard that compounds — if each ready-state-to-ready-state cycle touches $B$ with probability $q$, survival over $N$ cycles is $\approx (1-q)^N$, so a reassuring per-cycle $0.9999$ is $\approx 0.37$ over ten thousand cycles. The reach-avoid certificate for a daemon is therefore a bound on $q$ against the intended horizon — the safety twin of the regenerative expected time that replaces $V^\star_{\mathrm{ok}}$ for restarting specs.
-
-And the consolation rests in part on an assumption the world violates — though less of it than it first seems. The supermartingale *bound* itself survives a nonstationary kernel, provided the conditional drift holds uniformly at every step; what genuinely needs a **time-homogeneous kernel** is $V^\star$ as a fixed function, the resolvent / fundamental-matrix identities, and the sampled-$\delta$ calibration (which assumes the very kernel it was measured on). But the environment $E$ is *part of* $T$, and the world is not stationary — worse, it can be **adversarial**, an attacker choosing the tool-output *policy* — a kernel over what tools return, not the realized draw — so as to break your descent. The drift condition then stops being a fixpoint question and becomes a **minimax** one,
-
-$$\sup_{\alpha \in \Pi}\ \int_{\mathcal{Y}}\!\int_{\mathcal{E}} V\big(\rho(s, y, \gamma(s,y), e)\big)\, Q_E^{\alpha(s,y)}\big(s, \gamma(s,y), de\big)\; M_W(\pi(s), dy) \;\le\; V(s) - \varepsilon,$$
-
-a descent that must hold in expectation over the model's own output $y$ *and* even when the adversary picks the worst admissible environment policy $\alpha(s,y)$ from the class $\Pi$ of policies the environment genuinely permits — every $\alpha\in\Pi$ must still respect rejection, $\gamma(s,y)=\bot \Rightarrow Q_E^{\alpha}(s,\bot,\cdot)=\delta_{e_0}$, or the adversary resurrects side effects the gate refused. Well-posedness is a frontier caveat of its own: for $\sup_{\alpha\in\Pi}$ to be *attained* rather than merely defined, $\Pi$ needs structure — measurability of $\alpha\mapsto Q_E^{\alpha}$, compactness of the per-state admissible set, or a measurable-selection theorem furnishing a worst-case $\alpha$ — and "respects rejection" is a *constraint* on $\Pi$, not that existence argument; on a general state space the sup may have no maximizer, in which case the certificate quantifies over a maximizing sequence rather than a single adversary. A $V$ that certifies halting against a benign world is defeated by an adversarial one, and the measured $\delta$ bounds only the $Q_E$ you *sampled*, never the policy an attacker will choose.
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-**This is the formal home of prompt injection** — not "the model did something bad," but the environment optimized to bend your dynamics. And the target is not merely non-halting: injection steers toward a **bad set** $B$ — wrong acceptance, data exfiltration, unauthorized tool use, privilege escalation, irreversible side effects — so security is a **reach-avoid** problem, not a liveness one.
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-Here two reliability objects must be kept apart, because under absorbing refusal every naive intermediate collapses into one of them:
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-$$p_{\mathrm{succ}}(s) = \Pr_s\big(\tau_{H_{\mathrm{ok}}} < \tau_F\big), \quad F = B \cup (H \setminus H_{\mathrm{ok}}), \qquad\qquad p_{\mathrm{safe}}(s) = \Pr_s\big(\tau_B = \infty\big).$$
-
-**Success** is reaching a correct halt before *any* failure — a safe refusal counts *against* it. **Safety** is never entering the bad set at all — a safe refusal *satisfies* it. These genuinely differ on any run that avoids $B$ without reaching $H_{\mathrm{ok}}$ ($p_{\mathrm{succ}}$ scores $0$, $p_{\mathrm{safe}}$ scores $1$): safe refusals, and — absent almost-sure absorption into $H\cup B$ — safe non-halting or endless safe retry. The tempting middle form $\Pr_s(\tau_{H_{\mathrm{ok}}} < \tau_B)$ is *not* a third object, by a two-line case analysis: for it to differ from $p_{\mathrm{succ}}$, a run would need $\tau_F < \tau_{H_{\mathrm{ok}}} < \tau_B$ — a non-accepting terminal hit strictly before success, then success anyway — which forces *exiting* $H \setminus H_{\mathrm{ok}}$, impossible while $H$ is absorbing. Note what does **not** re-separate them: within-run fail-closed retries (the non-terminal fail-closed of the definition) never touch $F$ at all — the rejected proposal lands in a safe *non-terminal* state — so a refuse-retry-succeed run scores $1$ on both forms, and the coincidence survives any amount of retrying. The middle form becomes a genuine third object only when the two hitting times can genuinely part ways: under **restarting specs**, where an owner re-launches out of a refusal terminal and the absorbency of $H \setminus H_{\mathrm{ok}}$ is deliberately dropped (the regenerative reading the daemon note above already contemplates) — no bookkeeping needed, since hitting times record *visits*, not occupancy, so the relaunched run's $\tau_F$ is already finite — or under a failure set that counts refusal *events* accumulated in $s$, $F' = B \cup (H \setminus H_{\mathrm{ok}}) \cup \{\mathsf{refusals} \ge 1\}$, which separates the forms even within a single run. In the restart case a run may halt refused, restart, and still reach $H_{\mathrm{ok}}$ before $B$: the middle form credits it; $p_{\mathrm{succ}}$, measured against the refusal it passed through, does not. Safety is certified by a barrier / avoidance certificate for $B$; success needs that plus the reach part — a hitting-time drift toward $H_{\mathrm{ok}}$. Fail-closed control is the disturbance-rejection margin for both, but split by reversibility: the gate $\gamma$ caps how far an adversarial world reaches into *side effects* and widens the gap to $B$ (it is the margin for the irreversible part), while $\rho$ validates the response and folds back, rejecting bad state after the action has run — which cannot undo an authorized side effect. In this language, security is robustness of the reach-avoid certificate.
-
-And injection is not confined to the post-model kernel $Q_E$: poisoned retrieval, prompt-injected pages, and malicious tool metadata enter through $\pi$'s *inputs*, before generation — so the adversary lives wherever untrusted content enters the state/context-construction pipeline, which is why input provenance and the gate $\gamma$ both matter, not post-hoc verification alone. And provenance is a *precondition* of the certificate, not just an entry point to police: partition $s$ into a **control-determining** part — plan, intent, what is authorized next, the coordinates $\pi$ lowers and $\gamma$ checks — and a **data** part — tool values, retrieved text, the bytes of $e$. Reach-avoid presupposes untrusted effects touch only the latter; let $\rho$ fold attacker-controlled $e$ into the control part and the structural-intent check validates against a plan the adversary already bent, collapsing $\gamma$ to the strength of $\rho$'s validation. So the claim is conditional — reach-avoid *given* control flow provenance-isolated from untrusted data, the isolation that makes provable security possible (the content of CaMeL's control/data-flow separation, untrusted data filling typed values but never the program), a structural property the harness supplies and $\rho$ cannot recover after the fact. The partition then forces a question the isolation rule alone cannot answer: *something* must be permitted to write the control-determining part mid-run — or no plan could be steered, no approval granted, no scope widened — and naming that something is part of the object. It is the **trusted principal**: the owner of the run. An approval request is an ordinary authorized action through $\gamma$ into $Q_E$ — ask-the-owner is a tool call to the one counterparty you trust — and its response is the *single* class of $e$ that $\rho$ may fold into control coordinates; every other $e$ folds into data. This is not an exception eroding the partition but the partition completed: a provenance *lattice* with exactly one writer at the top, which is what trusted means — and the appendix's gate-placement entry derives the matching rule for *learned* verdicts, which may never stand in this writer's stead. One distinction keeps the lattice from outlawing the loop it governs. Control-determining is not one rank but two: **authority** — grants, scopes, budgets, what the principal has permitted — which only the top writer widens; and the **plan**, which the model rewrites at every fold of $y$, because replanning *is* the harness. The plan is a *middle* rank: written through the gated fold of the model's own output — the channel the minimax descent above already prices — never directly by an effect, and never a source of widened authority. The rank is also the field's live design axis: pin plan-writes to the top-derived rank — the plan fixed from the trusted query before any untrusted read, which is CaMeL's move — and provable security follows exactly there; let the middle rank replan interactively and you pay the adversarial price the certificate quantifies. A corollary with teeth: a dedicated planning component is rank-neutral — its writes land in the same middle rank as the model replanning inline — so it changes no guarantee and lives or dies on measured capability alone; in general, sub-components that only write middle-rank state are priced by evals, not by the certificate, which prices only rank crossings, gates, and $\Pi$. (For $B$ to capture irreversible side effects rather than only states, the side-effect ledger must itself live in $\mathcal{S}$, and the response $e$ must be an *effect record* carrying the ledger outcome — not just API bytes — since only $\rho$ writes external effects into $s$.)
-
-There is a **second wall, orthogonal to the first.** It binds not the full harness state $\mathcal{S}$ but the **model-visible working memory** $\mathcal{C} = \mathcal{V}^{\le L}$ — bounded by the context length $L$. That bound is *not* the incompressibility of $V^\star$ (a fact about the parameters $W$ — the **dictionary**, fixed at training); it is a fact about the inner kernel's **working memory** (the $L\times d$ residual stream — the **desk**). $\mathcal{S}$ itself may be far richer — files, databases, vector stores, durable memory, queues — but that is *external* memory the shell supplies, and the distinction is the point: every external read still passes *through* the $\le L$ window to touch computation, so external stores extend addressable storage without extending the per-pass resident set. The shell can page; the plant cannot grow its desk. (What follows is heuristic, not definition-level: the complexity claims turn on depth, precision, and architecture, and belong with the frontier, not the core.) The tape picture comes from the autoregressive structure alone and needs no complexity theorem: each step reads a bounded window and writes one token, so **the context window is the tape, the autoregressive loop is the read/write head**, and — in the variable-$L$, fixed-precision idealization — the model-mediated inner computation behaves like a linear-bounded automaton, its reachable fixpoints capped by space-$O(L)$ computability (chain-of-thought is register-spilling onto that tape). Separately, and more weakly, there is a *per-pass* expressivity bound: under the standard fixed-depth, log-precision theoretical model a single forward pass is in constant-depth $\mathsf{TC}^0$ — *suggestive* for deployed models, not literal (real models use fixed-point precision and depth that grows with scale, and log-depth variants escape parts of it). These are different resources — the first bounds the *space* the loop addresses, the second the *depth* of one step — and only the space bound carries the $L$-wall; chaining them (one pass buys bounded depth, *therefore* the loop is space-$O(L)$) would be a non-sequitur, since per-step depth says nothing about the length of the tape the loop runs on. This is a *second* obstruction beside divergence, and it concerns *success*, not raw halting. Split the terminal set: let $H$ be any halt state (including fail-closed refusal) and $H_{\mathrm{ok}} \subseteq H$ the successful, accepting halts, with $V^\star_{\mathrm{ok}}(s) = \mathbb{E}[\tau_{H_{\mathrm{ok}}} \mid s_0 = s]$ taken on the process where $H \setminus H_{\mathrm{ok}}$ — halting wrong, refusing, failing closed — is *absorbing failure*, so a run that fails closed before acceptance has infinite accepting hitting time unless the spec explicitly restarts it — hence unconditional $V^\star_{\mathrm{ok}}$ is infinite whenever pre-acceptance failure has positive probability, which is why the workable reliability object is the success probability $p_{\mathrm{succ}}$ (above) or, for restarting specs, the regenerative expected time. Then $U_{\mathcal{H}}(L)$ — harness-relative, since the shell's decompositions and verified tools determine what can be paged or outsourced — is the set of tasks whose **irreducible per-step model-mediated working set** exceeds $L$ — not tasks whose *data* exceeds $L$ (those the shell can page), and not work that can be **discharged to a verified external tool** (a solver, interpreter, or compiler computes off-context). For a task in $U_{\mathcal{H}}(L)$ the raw chain may still hit $H$ — by failing closed, refusing, or returning a wrong answer — so $V^\star = \mathbb{E}[\tau_H \mid s]$ stays perfectly well-defined; what blows up is $V^\star_{\mathrm{ok}}$, the expected time to a *correct* halt, which is infinite under a formal success predicate, or undefined if no such predicate has been specified. The honest statement is about the finite-success domain, and it is *schematic* — a shape written in set notation, not a theorem, since $\mathrm{reachable}_{\mathcal{H}}(L)$ is exactly as informal as the working-set notion behind $U_{\mathcal{H}}(L)$: $\mathrm{dom}_{<\infty}(V^\star_{\mathrm{ok}}) \subseteq \mathrm{reachable}_{\mathcal{H}}(L) \setminus D$ — both the reachable set and the divergent set $D$ relative to $\mathcal{H}$. The two walls **trade** — *directionally, not as a literal exchange rate*: parametric memory $|W|$ and working memory $L$ press on the same budget along the pretraining-vs-inference-scaling axis, with no clean unit-for-unit substitution of one for the other. And the bound is inherent to *finite working memory*, not attention specifically: state-space models embody it differently (a fixed-size recurrent state rather than an $L$-window), and real attention's usable tape is shorter than $L$ (lost-in-the-middle).
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-## Where it cashes out
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-This is not ornament; the decomposition is load-bearing in the design.
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-- **$\pi$ is a progressively-lowered dialect stack** — raw input → intent → plan → tool-call → the neutral wire IR — each level a deterministic pass with its own verifier — *pass* and *verifier* meaning the shell's transformation and checking: the **content** entering at the plan level is plant-authored, middle-rank state (the two-rank note of *The limit*), which is exactly why that level carries a verifier at all. The per-step drift $r(s)=\mathbb{E}[\hat V(s_{n+1})\mid s]-\hat V(s)$ splits by coordinate, $r = r_{\text{shell}} + r_{\text{plant}} + r_{\text{env}}$ — presuming an additively separable $\hat V$, or a declared scheme attributing each step's drift to shell, plant, and environment coordinates: the shell term is an *exact, designed* descent — but per lowering pass, not per outer step: each pass strictly narrows the admissible-meaning set, a well-founded descent we build by hand, while the outer loop *revisits* — retry, replan, rewind are planned ascents of any reasonable $\hat V$, which the run-level certificate must absorb (a retry budget inside $\hat V$ is the standard device), so the shell's descent is well-founded in the nested, lexicographic sense rather than monotone along the run; the plant term ($M_W$) is the irreducible residue, and the environment term ($Q_E$) is the one an adversary controls — the very quantity the minimax descent must bound, which the old two-way split folded out of sight. **Syntactic soundness is free; semantic adequacy is not.** Relative to a formal schema and a correct validator, schemas, types, and boundary checks go into the shell at zero probabilistic cost; whether the lowered task still *means* what the user intended stays empirical, because natural language supplies no source-language standard to check against.
-- **$\rho$ is fail-closed verification** — validate at every boundary, never let malformed state flow downstream. The discipline transfers from compilers in *form*; the *teeth* do not, because a harness has no source-language standard — natural language is, in effect, all undefined behavior — there is no complete formal source-language semantics to check against. And $\rho$ must be *deterministic*: if verification is itself an LLM judge, that is another learned kernel call — it belongs in $M_W$, not in $\rho$. Where $\rho$ *repairs* rather than rejects — canonicalizing malformed input into valid shape — remember that repair is an authorization decision in disguise: each repair rule converts a reject into an accept on bytes the adversary chose, so it must be deterministic, meaning-narrowing, and its output re-validated as if it had arrived that way, or the repair pass is a bypass of the very boundary it serves.
-- **$\delta$, $\mu(D)$, $\mathrm{Var}[\tau_H]$ are what you measure** — not derive. You instrument the certificate precisely because the architecture does not hand it to you — you estimate it unless it is separately certified. And the meter is attack surface: if $\hat V$ is itself computed by a learned judge — a model scoring "progress" — the instrument is a kernel draw with the plant's own adversarial exposure, and an environment optimized to bend your dynamics will bend your *measurement* of them first; an injected page persuading the judge that work is advancing is precisely a divergence hidden from the dashboard built to catch it. The rule that put the LLM judge in $M_W$, not $\rho$, applies to instrumentation too: a learned $\hat V$ is part of the measured system, never a neutral meter.
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-## How this could be wrong
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-It is a hypothesis; here is what would falsify it. If the controller cannot in practice be kept deterministic — if real reliability demands stochastic control the plant can't absorb — the clean *deterministic* split is a fiction (the broader $K_C$ kernel model still holds, but loses its payoff: localizing every coin to the plant). If the drift slack $\delta$ turns out *not* to track real-world failure, the whole "measure the certificate you can't prove" program is empty. And if harnesses are simply better described some other way — not as nested stopped chains at all — then this is a pretty equation that merely happens to fit, an elegance we would be right to distrust.
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-First, handles — the load-bearing claims numbered, so the tests have addresses. **C1**: the harness is faithfully modeled as nested stopped Markov processes — the tuple, the outer $T$, the inner $M_W$. **C2**: the controller injects no randomness — every coin localizes to $M_W$ and $Q_E$. **C3**: fail-closed is a *gate* property — no effect crosses unvalidated, and rejection is a true no-op. **C4**: no certificate of correct halting comes free, and the measured slack $\delta$ is a calibrated risk metric, never a certificate. **C5** (conjecture): the minimal certificate $V^\star$ admits no representation materially below model scale. **C6**: two orthogonal walls — divergence ($\mu(D)$) and the $L$-bounded per-pass working set. **C7**: security is reach-avoid, certifiable only conditional on provenance isolation with a single trusted writer. **C8** (figure): certificate and interlingua are one object — already demoted by its own section, and exempt below accordingly.
-
-Each claim is operational, not merely rhetorical:
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-- **State-ablation (C1 — the Markov claim).** Drop a variable from $s$ and check whether next-step transition statistics move. If they do, the abstraction was not Markov, and $s$ must be augmented until it is. (Passing is necessary, not sufficient — the test can falsify Markovity, not establish it.) The same probe pointed at $\pi$ tests lowering *sufficiency*: drop a coordinate from $c$ rather than $s$ and watch task success rather than transition statistics — context compaction lives or dies by exactly this.
-- **Controller-determinism audit (C2).** Re-run with model samples and tool outputs *held fixed*. Any residual variance is randomness the harness itself injected — clock reads are the classic leak (timestamps folded into $s$, wall-clock timeouts, cache expiries) — and must be folded into $Q_E$ or the controller, or the determinism claim is false.
-- **Drift calibration (C4).** Test whether $\hat V$-drift actually predicts failure, retry count, latency, or non-halting. One uncorrelated candidate kills that candidate, not the program; the program is empty only if candidates from the natural families — plan depth, open-obligation counts, budget burn, judge scores — *systematically* fail to track failure.
-- **Adversarial-environment test (C7).** Replace sampled $E$ with worst-case tool outputs, prompt-injected documents, poisoned tool metadata, malformed responses. The minimax descent must survive these, not merely the benign draw.
-- **Boundary-control ablation (C3, C7).** Compare prompt-only defenses against deterministic tool-call validation, capability checks, sandboxing, and fail-closed rejection at the gate $\gamma$. The hypothesis predicts the latter class dominates; if prompt-only defenses match it, the controller/plant security story is wrong.
-- **Readout-typing check (C1, C3).** Verify that $M_W$'s codomain is exactly what $\gamma$ consumes — especially under window truncation, where the final context need not hold the full transcript, so the output buffer and the gate's input must still agree.
-- **Certificate-compression search (C5).** The conjecture falsifies constructively: exhibit a $\hat V$ of description length far below $|W|$ whose worst-case slack is provably $\le 0$ over a nontrivial task domain. The text concedes the live counter-possibility — coarse hitting-time functionals of complicated kernels are sometimes cheap — so C5 stands only until someone cashes it.
-- **Working-set probe (C6).** Fix the shell and scale a task family's irreducible per-step working set past $L$, on tasks the shell can neither page nor discharge to a verified tool — anchoring "irreducible" in families with proven streaming or communication-complexity lower bounds, so the floor is someone else's theorem and a solved family cannot retreat to reducible-after-all. C6 predicts success collapses at the wall rather than degrading smoothly; a family solved reliably past it, without new shell decompositions, falsifies the second obstruction.
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-## Where this points (the frontier — least falsifiable, so flagged)
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-If $V^\star$ is incompressible only in *token* coordinates, the right change of coordinates might compress it — and that change of coordinates is a representation of meaning itself. Cost-to-go and representation co-determine each other: where the Koopman operator is diagonalizable — a point-spectrum idealization, since mixing dynamics carry continuous spectrum and admit no eigenbasis — the eigenbasis that linearizes the dynamics is also the one in which the certificate decomposes, and even then only for a $V$ in the span of those eigenfunctions; in reinforcement learning the discounted successor representation (Dayan 1993) is the resolvent $(I-\beta P)^{-1}$ — discount $\beta$, not the gate $\gamma$ — with $V$ a *linear readout* of it — and in the undiscounted, absorbing case that actually matches a stopped harness the same role is played, in the finite setting — and countable settings where the Neumann series converges — by the **fundamental matrix** $N = \sum_{n \ge 0} Q_{\mathrm{tr}}^{\,n}$ (written $(I - Q_{\mathrm{tr}})^{-1}$ when the inverse exists), where $Q_{\mathrm{tr}}$ is the sub-stochastic kernel restricted to $H^c$ (transitions before absorption at $H$) and the row sums $N\mathbf{1}$ *are* $V^\star$ on the finite-mean hitting domain; on general state spaces the same series is read as the potential (Green) operator $G$, with $G\mathbf{1} = V^\star$ wherever it converges. Each of these is a clean identity only for a fixed, time-homogeneous kernel — under a nonstationary $Q_{E,n}$ the resolvent and fundamental matrix dissolve into a time-ordered product, and under an *adaptive* adversary into a controlled / game-value operator, so what is identity in the stationary regime is analogy beyond it.
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-With that caveat, **the interlingua and the certificate are one object seen twice** — and the reason neither can be written in closed form is the same "all undefined behavior": no canonical lowering of meaning, hence no finite header-file for either. The only representation of both is $W$ — a band-limited, lossy compression of a scale-free meaning-space, sharp where the record is thick and blurred where it thinned. That a finite object renders an infinite one *lossily but honestly* — declaring its resolution, and where it is unsure — is not a lie; it is the most an $f(\cdot\,;W)$ can do. **The search for $V$ and the search for the interlingua are not two programs. They are one** — and the day either is written in closed form, so is the other, or we will have proven why neither can be. Read this as *figure*, not a lurking theorem: the only precise version would need the Koopman eigenbasis to fall on the very coordinates that lower meaning, and the mixing-spectrum caveat above already concedes that eigenbasis does not exist — which guts it. It is the least-defensible claim in this document, and it should announce that rather than imply a rigor it has not got.
-
----
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-*The formula is the architecture; the corollary is why the architecture is hard. Both on the page — nothing hidden behind a tidy composition.*
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-## Grounding
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-Borrowed theorems are real; the framings are not — keep them separate. Some framings are nonetheless *corroborated* — independently reached from another field — a third grade, weaker than proof and noted last.
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-**Proven (citable).** Foster–Lyapunov drift ⇒ positive recurrence + $\mathbb{E}[\tau]\le V(s_0)/\varepsilon$ (Foster 1953; Meyn & Tweedie, *Markov Chains and Stochastic Stability*, 1993) — positive recurrence needs the usual irreducibility/petite-set hypotheses, while the absorbing-halt case used here needs only the weaker supermartingale optional-stopping hitting-time bound. The minimal $V$ is the expected hitting time, by first-step analysis + optional stopping (Norris, *Markov Chains*, 1997). For an absorbing chain that expected hitting time is the row sum of the fundamental matrix $N=\sum_{n\ge0}Q_{\mathrm{tr}}^{\,n}$ (Kemeny & Snell, *Finite Markov Chains*, 1960), with the general-state analogue the potential (Green) operator (Revuz, *Markov Chains*, 1984). Koopman's linear-operator view of nonlinear dynamics is classical (Koopman 1931), and Lyapunov functions can be assembled from its eigenfunctions when the spectrum is suitable (Mauroy & Mezić, 2016). You certify a candidate $\hat V$ by a *proven* drift inequality rather than by deriving $V^\star$, and estimate it empirically only where a proof is out of reach — the empirical drift checks, it does not certify (neural-Lyapunov: Chang, Roohi & Gao, *Neural Lyapunov Control*, NeurIPS 2019, arXiv:2005.00611). A classical monotone data-flow analysis gets its $V$ for free because a finite-height lattice is a well-founded descent (Kildall, POPL 1973). The gate-a-plant architecture itself is classical: supervisory control theory synthesizes a deterministic supervisor that disables controllable events of a plant it does not author, with the supremal controllable sublanguage as the largest admissible behavior (Ramadge & Wonham, SIAM J. Control and Optimization, 1987) — $\gamma$ is that supervisor, with a learned stochastic plant on general state spaces. The successor representation is Dayan (*Improving Generalization for Temporal Difference Learning: The Successor Representation*, Neural Computation 1993). Dialect-stack architecture: MLIR (Lattner et al., CGO 2021, arXiv:2002.11054); learned pass-ordering: MLGO (Trofin et al., arXiv:2101.04808). Single-pass low-depth expressivity: log-precision transformers are simulable by constant-depth logspace-uniform threshold circuits ($\mathsf{TC}^0$) (Merrill & Sabharwal, *The Parallelism Tradeoff: Limitations of Log-Precision Transformers*, TACL 2023) — fixed/constant precision is a stronger restriction, added autoregressive steps escape it (Merrill & Sabharwal, *The Expressive Power of Transformers with Chain of Thought*, ICLR 2024), and growing precision changes the picture, so the bound is suggestive for deployed models, not literal.
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-**Asserted (ours — not theorems).** That the harness is best modeled as nested stopped chains; that $V^\star$ is incompressible (no compression theorem); that "no lattice for $f(\cdot\,;W)$" means none is *known*, not that none exists; and everything under *Where this points* — including the Koopman/certificate co-determination, which is well-posed only under the spectral assumptions noted there, and the interlingua/certificate identification; and the design rules read off the objects rather than proven from them — the single-trusted-writer completion of the provenance partition, the narrow-only rule for learned checks, the composition law of the appendix. These organize the design; they are not results.
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-**Converged-upon (independently arrived at, from other framings).** The *Asserted* claims above are ours but not ours alone; several are reached independently, from starting points unconnected to this framing — which is the corroboration a definition earns: not a chorus of agreement (the systems below often disagree on method and goal), but that work approaching from capabilities, reinforcement learning, control theory, software architecture, and language-modeling theory each lands on a piece of the same object. That the **deterministic controller, not the model, carries the guarantee** is reached from four directions — capability and information-flow control (CaMeL: Debenedetti et al., *Defeating Prompt Injections by Design*, arXiv:2503.18813, securing the agent even when the underlying model is susceptible); reinforcement learning (shielding: Alshiekh et al., *Safe Reinforcement Learning via Shielding*, AAAI 2018, arXiv:1708.08611 — a deterministic reactive shield filtering a learned policy's actions against a temporal-logic specification); control theory (*Stable Agentic Control*, arXiv:2605.03034, enforcing finite action catalogs at the tool-output interface under a Lyapunov input-to-state-stability certificate against adversarial disturbance); and software architecture (the plan-then-execute / control-flow-integrity line, e.g. Beurer-Kellner et al., *Design Patterns for Securing LLM Agents against Prompt Injections*, arXiv:2506.08837). The **certified-vs-measured split** is reached from the construction side (CaMeL's provable security) and, independently, from the destruction side (guardrail-evasion results — *Bypassing Prompt Injection and Jailbreak Detection in LLM Guardrails*, arXiv:2504.11168, the v1 title — later versions retitle it; *No Free Lunch with Guardrails*, arXiv:2504.00441), with verification-oriented work stating it as the motivating gap (*Towards Verifiably Safe Tool Use for LLM Agents*, arXiv:2601.08012; VeriGuard, arXiv:2510.05156): a learned safeguard raises the odds of detection but cannot guarantee safety against a persistent attacker. The **inner readout as a composition of Markov kernels** is independently formalized in language-modeling theory — the autoregressive step as kernel composition in the category $\mathsf{Stoch}$ (*A Markov Categorical Framework for Language Modeling*, arXiv:2507.19247), and the broader "LLMs as Markov chains" line — though that work models the inner kernel alone and never closes it into an agentic loop, which is exactly the seam this definition adds. That **provenance shrinks the admissible adversary** is reached by datamarking / spotlighting (Hines et al., arXiv:2403.14720, 2024) and by CaMeL's data/control-flow separation; and a systematization of prompt injection against agentic coding assistants reaches the same verdict from the attack side — mitigation must be *architectural*, not model-level (*Prompt Injection Attacks on Agentic Coding Assistants*, arXiv:2601.17548); the sharper open problem this object is built to answer — formally specify the trust boundaries, then verify implementations respect them — is our phrasing of where that verdict points, not the paper's. Two convergences are weaker, and flagged. The **reach-avoid hitting-time certificate** is the independently developed reach-avoid supermartingale (RASM, arXiv:2210.05308, AAAI 2023) and stochastic Lyapunov–barrier apparatus, and its *hardness* is corroborated — expected-stopping-time problems for Markov chains are inter-reducible with the Positivity problem, a relative of the Skolem problem (Chatterjee & Doyen, *Stochastic Processes with Expected Stopping Time*, arXiv:2104.07278) — but this supports generic hardness only, not the specific incompressibility-at-$|W|$ conjecture, which remains ours and unproven. And **injection as an adversarial policy** is corroborated as a minimax game in the *detection* setting (DataSentinel: Liu et al., *A Game-Theoretic Detection of Prompt Injection Attacks*, arXiv:2504.11358) and as adversarial-disturbance robustness (*Stable Agentic Control*, above) — but no prior work assembles it as reach-avoid over the tool-output kernel with the gate as the irreversibility margin; here the relation is adjacency, not convergence.
-
----
-
-## Appendix: model implementation
-
-The definition is deliberately abstract: $\pi, \gamma, Q_E, \rho$ are *roles*, not code, and a deployed harness forces concerns the abstract object is silent on. This appendix does not re-derive the implementation; it establishes a **pattern** — take a hard practical concern, locate it in the objects already defined, and read off the discipline they imply rather than inventing new machinery. Cancellation is the worked example, chosen because it is where the silence bites hardest and because the answer falls entirely out of objects already on the page.
-
-**Cancellation.** An owner stops a running agent mid-flight — worst across a task-agent tree. The naive reading is "stop and undo," but the irreversibility point forbids it: $\gamma$ is the last line before irreversible effects, and $\rho$ can reject a response but cannot undo an authorized action. So cancellation is not *making it not have happened*; it is a disciplined stop with a defined disposition for what is already irreversible.
-
-A cancel is a signal, so by the Markov requirement it lives in $s$. The gate then closes on it: while the cancel flag is live, $\gamma(s,y)=\bot$ for every proposal. That is the entire "block the pending actions" requirement — they hit the gate already built and bounce into the no-op, with no new blocking machinery — and it forecloses all *future* turns at once, since $\pi$ lowers nothing new that $\gamma$ will pass. After the signal is observed, **no action crosses $\gamma$.**
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-The hard half is the action already *past* $\gamma$, executing in $Q_E$, whose effect is landing or has landed. Here the disposition is a trinary on the kind of $Q_E$ you authorized. If the tool is **cancellable**, propagate the cancel into it; it aborts and reports a true end-state (committed, rolled-back, or partial), and $\rho$ folds the real disposition. If it is **bounded** — drainable in acceptable time — simply wait and record the real $e$. If it is **opaque and unbounded** — a bash invocation that may itself be a harness, an environment you hold no handle into — you cannot stop the effect, only your *wait* for it: the controller fabricates $e$, a synthetic "cancelled" response, and folds it through $\rho$ so the loop can reach a terminal.
-
-That synthetic result is the subtle case, and the load-bearing rule is this: $\rho$ may fabricate the *acknowledgment* but must not fabricate the *outcome*. A synthetic "cancelled, no effect" entry reads downstream as *the action did not happen* — and will cause a double-send exactly as readily as a dropped record causes an orphan. Same bug, opposite sign. An outcome you did not observe is $\mathsf{unknown}$, never $\mathsf{none}$: the cancelled agent never saw whether bash sent the email, and the ledger must say exactly that. (This is why $e$ must be an effect record and the ledger must live in $s$ — the fabricated entry is still a ledger write, and its value is what a later reader acts on.)
-
-The run halts into $H_{\mathrm{cancel}} \subseteq H \setminus H_{\mathrm{ok}}$ — a distinguished terminal, non-accepting but *safe* (outside $B$), refining the deliberately coarse $H \setminus H_{\mathrm{ok}}$ of the definition (the body leaves that set unenumerated; the appendix is where its subclasses earn names) — with a specific postcondition: no action crossed $\gamma$ after the cancel was observed, every in-flight action was drained to its real disposition or recorded $\mathsf{unknown}$, and the ledger is consistent. It is worth separating from refusal and from a wrong answer precisely because that guarantee is its own.
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-Cancellation must be **cooperative, not preemptive.** The owner writes the cancel into the child's $s$; the child observes it at its next $\gamma$ check. The guarantee is therefore "no new action after the cancel is *observed*," not "after it is *sent*" — a child may authorize one more action in the gap, which simply drains like any other in-flight. Preemptive cancellation — killing the child mid-$Q_E$ — is exactly what manufactures $\mathsf{unknown}$ state at scale, because it destroys the record of whether the action landed. And the propagation is **recursive**: cancel flows down the subtree, each level closes its gate at its next check and drains, and the owner's cancel "completes" only when the subtree has drained. A single agent's drain is its own in-flight action; a tree's is the whole subtree reaching safe points cooperatively — the irreversibility problem stacked on a distributed-coordination one, which is why task agents are the worst case.
-
-Compensation lives **outside** the cancelled agent. A completed-but-unwanted effect cannot be undone by the agent that caused it — its gate is closed — so a compensating, saga-style action is the *owner's* job, issued after $H_{\mathrm{cancel}}$ and reading the child's ledger to decide what to reverse or annotate. It must be the owner's, because the cancelled child cannot even know whether compensation is needed: it never observed the outcome. The owner inherits the $\mathsf{unknown}$ and any still-live orphan process, and reconciliation is its responsibility.
-
-Finally, the part that shapes the tool rather than the document. Opaque unbounded $Q_E$ is uncancellable because authorization happened at the wrong **granularity** — an unbounded environment crossed $\gamma$ on a single approval. The discipline the objects imply is therefore not "handle uncancellable tools better" but: *the gate should prefer bounded, instrumented $Q_E$ over opaque ones, so that cancellation and the ledger stay honest.* A bash invocation behind a wrapper that tracks its process tree and effects converts the third branch into the first. Sometimes opaque is the only option, and then $\mathsf{unknown}$ and owner-inherited orphans are the honest floor — but where the choice exists, that is the pressure cancellation semantics put on tooling.
-
-**Resume (involuntary stop).** Cancellation's twin, without the courtesy of a signal: a process crash, a lost node, a partition mid-$Q_E$. Nothing new is needed to say what recovery *is*. A crash is not a halt — $H$ is a property of the state, and the run never reached it; the chain merely stopped being *computed*, and resume computes it further, re-entering $T$ at the last durable $s$ (not the body's *restarting spec*, which exits a refusal terminal — here no terminal was ever reached). That sentence is the Markov requirement cashing out operationally: re-entry is sound exactly when $s$ was the whole state, so anything load-bearing that lived only in process memory — an in-flight buffer, a lock held in RAM, a plan revision not yet folded — is a state-ablation failure (*How this could be wrong*) discovered at the worst possible time. Durability of $s$ is not an implementation nicety; it is what the Markov claim *means* when the machine dies.
-
-The sharp part is an ordering the ledger's own trichotomy forces. The formal transition is atomic — $s_{n+1} = \rho(s, y, a, e)$ in one piece — and a crash lands *inside* it, so resume is really a statement about the implementation's refinement of that atom into micro-steps: authorize, journal, dispatch, collect, fold. The discipline is that every crash point must resume to one of exactly two honest readings — not-yet-dispatched ($\mathsf{none}$, safely retriable) or dispatched-unconfirmed ($\mathsf{unknown}$, the cancellation entry's third branch) — and **journal-before-dispatch** is what makes the boundary between them observable: on $\gamma$'s authorization the shell journals an open $(\mathsf{action\_id}, \mathsf{pending})$ entry into durable $s$ before $Q_E$ sees the action — the write is the shell's step bookkeeping, so $\gamma$ itself stays effect-free. Journal *after* dispatch and a crash in the gap leaves no record at all — resume reads silence as $\mathsf{none}$ and re-sends, the double-send bug again, produced by a power cut instead of a synthetic entry. Write-ahead intent is not imported from database lore; it is forced by "did not confirm" is not "did not happen."
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-The same pressure lands on tooling from a second direction. The $\mathsf{action\_id}$ the record already carries is an idempotency key wherever the tool will accept one: re-dispatch after resume becomes safe, and $\mathsf{unknown}$ becomes *queryable* — ask the tool what it did with this key — rather than terminal. The disposition trinary returns with new labels: idempotent-or-queryable $Q_E$ resumes cleanly, bounded $Q_E$ drains, opaque $Q_E$ leaves $\mathsf{unknown}$ and owner-inherited orphans, the honest floor again. The wrapper that made bash cancellable makes it resumable; it was the same wrapper all along. And if durable $s$ itself is lost there is nothing to re-enter: the run collapses to a single $\mathsf{unknown}$ in its owner's ledger — degraded accounting, but never silent.
-
-**Gate placement (fail-closed, in practice).** The natural implementation question is whether fail-closed means tool-call parsing and validation must happen before any tool invocation. It does — with the division of labor the definition already fixed: *parsing* lives in the inner readout $R$, the syntactic, verified extraction into $\mathcal{Y}$ (what the readout-typing falsifier checks), and *authorization* lives in $\gamma$, which is a *gate* — validation is not merely *prior to* invocation, it is what *authorizes* it. The model emits text; $R$ has already extracted it into a typed proposal; $\gamma$ validates that proposal against $s$, and only a survivor becomes an authorized action that $Q_E$ may execute. The teeth are in $\gamma$ being the *sole* route from model text to execution: no path to a side effect that does not pass the gate. And the validation is not a fixed checklist but **any deterministic predicate over $s$ and $y$** — that domain is the point, since the gate sees all of the state and the full proposal, so anything computable from them is a legitimate authorization condition. Three kinds matter. *Syntactic* — well-formed, schema-conformant, the tool exists, arguments typed. *User authorization* — does the principal this run acts for hold the right to *this* operation on *this* resource in *this* context: a function of the auth scope, principal, and session carried in $s$ and the resource and operation named in $y$, and *dynamic* rather than a static capability table, since the same caller may be permitted now and not once a budget is spent or a lock held. *Structural intent* — does the call cohere with the plan and the lowered task already in $s$: a consistency check, not a mind-reading one.
-
-That last kind marks the seam where the gate stops being able to stay pure, and it is the same seam the rest of this document is built around. The *structural* slice of intent — does the action cohere with the plan in $s$ — is a deterministic predicate over $s$ and $y$, effect-free, and belongs in $\gamma$ without reservation. But whether an action matches what the user *actually meant*, in the full semantic sense, is exactly the thing the definition says cannot be checked: natural language is all undefined behavior, with no source-language standard to validate against. So a semantic intent check is a *learned* check, and an LLM judging "is this what they wanted" is a **stochastic kernel** — putting it inside $\gamma$ breaks the property the gate exists to hold, by the same move flagged for the fold-back verifier: a learned judge is a kernel, and belongs in $M_W$, not in a deterministic map. Semantic intent therefore does not live *in* the gate; it is a plant call — a separate authorize-the-proposal pass through $M_W$ whose output $\gamma$ then deterministically gates — or it is drift you measure, never a guarantee you hold. That nested call is not a new kind of thing: it is a mini-harness inside the gate's decision — a judge $M_W$, its own syntactic readout, its own deterministic gate — so its failure case answers itself, the inner gate fail-closing on an unparseable or low-confidence judgment exactly as the outer one does, because it *is* one. The object is **closed under this construction**: semantic gating is added by recursion, not by a new primitive. One constraint on the recursion is load-bearing enough to be a rule, because it is where this entry meets the provenance partition of the body: the judge's verdict is derived, through a learned kernel, from the very content an adversary may have bent, so folding it into authorization is exactly the fold the partition forbids — *unless the verdict can only cost capability*. **A learned check may narrow the deterministic admissible set; it must never widen it.** Judge-as-veto is safe by construction: attacker influence over the judge can at worst manufacture a denial, a liveness cost the certificate already prices. Judge-as-approver — a verdict granting what the deterministic checks alone would refuse, or standing in for the trusted principal's confirmation — lowers the certified floor to those deterministic checks alone; if avoiding $B$ depended on the deny the judge now withholds on the adversary's behalf, the certificate is gone. Only the trusted principal widens authorization; learned kernels only narrow it. (The recursion already obeys this: the mini-harness's inner gate fail-closes to $\bot$ — a deny — which is why the construction was safe to add at all.) The cost is real and worth stating — a judge pass is another full model call, with its latency and tokens — so it is a decision about *which* actions warrant it, not a free wrapper for all of them. The gate widens to every deterministic predicate over $s$ and $y$; it does not widen to the one predicate the document says is not deterministically checkable.
-
-But "before any invocation" has to be read as *before any effect*, which is sharper than it sounds — and the reason is the irreversibility point above: you validate before execution because execution is what you cannot take back, so the real invariant is **no effect crosses $\gamma$ unvalidated**. That catches three cases the naive reading misses. *Reads are not free*: a read-only call is still an injection vector (it pulls attacker-controlled content into context) or an exfiltration vector (a request whose URL is the payload), so the gate authorizes the *call* regardless of whether it mutates. *Validation must not act*: a "validator" that resolves a call by hitting an API, expanding a template that fires a webhook, or evaluating an argument that runs code has collapsed validation into invocation, and the effect has already happened *inside* $\gamma$ — so $\gamma$ itself must be **effect-free**, pure and total over the proposal and the current $s$, with no network and no execution; if deciding validity *requires* a side effect, that side effect is itself an action and must go through the gate, recursively. *The output is an action too*: the user-visible response and any logging are effects — for model-authored text, emitted either as an authorized action through $\gamma$ or only after an accepted halt (shell-templated status on any halt is the controller speaking, not the model) — streaming raw tokens to a sink before $\gamma$ has cleared them is the same bug from the other end.
-
-So the property, tightest: $\gamma$ is a **pure, effect-free authorization that every model-proposed action — tool call, read, write, or final output — must pass before any effect occurs**, with "before" enforced structurally by the gate being the only route from model text to $Q_E$. The two failure modes to design against are a path from model output to a sink that bypasses the gate, and a $\gamma$ that is not effect-free, so that "validating" a call already rang the bell. And the boundary, so the property does not overpromise: $\gamma$ guarantees *no unauthorized effect* — pure code ordering, fully in your control — but not that an *authorized* effect is safe or correct; that is the plant's problem, and the reason $\rho$ and the reach-avoid certificate exist. Fail-closed is the floor — nothing executes that did not pass the gate — not the ceiling.
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-There is a third failure mode beside those two, and it is not a code path but a credential. A tool process that holds standing authority — an environment full of long-lived secrets, a database connection with every grant, an agent identity the network trusts — does not need the model's proposal to act, and against it $\gamma$'s $\bot$ is a decision with nothing to enforce it. The gate *decides*; something must make the decision *binding*, and "no path from model output to a sink that bypasses the gate" must be read to include the non-code paths: ambient authority is a bypass provisioned before the run began. The discipline is **per-action capability**: the authorized action *carries* its grant — a scoped, short-lived credential minted at authorization, valid for this $\mathsf{action\_id}$, this resource, this operation — so that a tool holds, at any moment, exactly the authority of the actions the gate has passed it and nothing standing. In the language of the minimax certificate this is enforcement as $\Pi$-shaping: sandboxing, capability scoping, and network policy do not make the gate smarter — they shrink the class $\Pi$ of environment policies an adversary can choose from, so the worst case the certificate must survive gets structurally smaller. A gate in front of an omnipotent tool is a suggestion; the objects compose into a guarantee only when $Q_E$'s reachable effects are no larger than what crossed $\gamma$.
-
-And one more boundary, because "fully in your control" above is a *single-run* statement. $\gamma$ authorizes against the $s$ it read; the effect lands later, against a world that may have moved — the gate cannot freeze the world between authorization and commit, so the honest property is *no effect unauthorized relative to the $s$ at authorization time*, and closing that gap requires the tool itself to bind check to commit (compare-and-swap in $Q_E$), which relocates part of the enforcement past the gate and weakens "$\gamma$ is the last line" to "$\gamma$ plus a commit guard" for exactly the effects that need it. The same seam opens *between* runs: the dynamic authorization state the gate reads — budgets, quotas, locks — is, once shared, no single run's coordinate, and two children of a coordinator can each pass $\gamma$ against snapshots that jointly overdraw a budget neither exceeded alone. The cancellation entry's observed-not-sent gap ("a child may authorize one more action in the gap") is this phenomenon wearing one hat; the general statement is that cross-run authorization state needs its own serialization discipline — the ledger as the serialization point is the natural choice — and the per-run certificate is silent about it. TOCTOU is not a counterexample to the formalism; it is what the formalism says when you admit $s$ is a *view*.
-
-**Parallel proposals (the batch gate).** Models emit several tool calls in one turn, and the outer chain assumed one action per step. The repair is formally cheap: a batch is a single action in $\mathcal{A}$ that happens to be a set, $Q_E$ runs its elements concurrently, the interleaving's nondeterminism folds into $Q_E$ exactly as the determinism audit requires, and $\rho$ folds one effect record per element — $e$ is then a finite set of records — each keyed by its own $\mathsf{action\_id}$ — the record interface already supports partial outcomes (one element $\mathsf{committed}$, its sibling $\mathsf{unknown}$). One discipline survives the cheapness: **individually admissible actions can be jointly inadmissible.** Read-the-secret and post-to-the-web each pass a per-call check; the pair is an exfiltration channel — and two calls that each fit a budget jointly overdraw it, the cross-run overdraw of the previous entry reappearing *inside* one turn whenever elements are authorized independently. Since $\gamma$'s domain is any deterministic predicate over $s$ and $y$, joint authorization was licensed all along; the content here is only that the gate must take it — authorize the *set*, atomically, against one snapshot, with interaction predicates (source-to-sink flow between capability classes, summed resources) and not merely element predicates. The cost note is the judge's, transposed: full powerset reasoning is combinatorial, so a real gate checks declared interactions rather than every subset — a tractability trade to make explicitly, not by forgetting the batch was a set.
-
-**Effect records (what $\rho$ folds back).** The fold-back $\rho$ and the cancellation ledger both turn on the response $e$ being an *effect record* rather than raw API bytes — said twice in the body and pinned down nowhere, though it is the interface that makes both tractable. The minimal shape is small: roughly
-
-$$e = (\mathsf{tool\_id},\ \mathsf{action\_id},\ \mathsf{status},\ \mathsf{effects},\ \mathsf{time}), \quad \mathsf{status}\in\{\mathsf{committed},\mathsf{rolled\_back},\mathsf{partial},\mathsf{none},\mathsf{unknown}\}, \quad \mathsf{effects}=[(\mathsf{resource},\mathsf{op},\mathsf{reversible})].$$
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-Each field is forced by something the body already needs. The $\mathsf{action\_id}$ lets $\rho$ match a response to the in-flight action $\gamma$ authorized, and lets the ledger say which actions are still open — without it the $\mathsf{unknown}$/orphan accounting has nothing to key on. The $\mathsf{status}$ must carry $\mathsf{unknown}$ as a value *distinct* from $\mathsf{committed}$ and from $\mathsf{none}$, because that distinction is the whole content of the cancellation ledger: "did not confirm" is not "did not happen" ($\mathsf{none}$ is *never launched* — the record of the distinguished no-op $e_0$ a $\gamma$-rejection forces, which is how a bounce at the gate enters the ledger at all — distinct in turn from $\mathsf{rolled\_back}$, which launched and was undone: conflating those erases the difference between a gate that held and a compensation that worked). The $\mathsf{reversible}$ bit on each effect is what lets the gate know which effects are irreversible — the predicate the gate-placement entry leans on ("anything irreversible must be gated at authorization") but cannot evaluate unless the record carries it (a bit is the minimal honest form, not the final one: real effects are reversible *until* — an unsend window, a force-push until someone fetched, a row until the backup rotates — so the mark wants to be a $(\mathsf{reversible\_until}, \mathsf{cost})$ pair, a refinement the open-interface caveat below already licenses). And $\rho$ writes the record into $s$ (the ledger lives in the state), which is what lets the next step's $\gamma$, and any owner-side compensation, read it at all. The exact fields are an **open interface, not a result**: bash, HTTP, a filesystem, and a database expose effects at wildly different granularity, and a record uniform across them is a real design problem this document does not resolve — it fixes only what the record must *support* (match by $\mathsf{action\_id}$, the $\mathsf{committed}$/$\mathsf{none}$/$\mathsf{unknown}$ trichotomy, and a reversibility mark), since without those three $\rho$ and the cancellation semantics lose their grip.
-
-**Derived and durable state (compaction and memory).** Two mechanisms let data re-enter the context long after it arrived: compaction, which replaces transcript with a summary when the conversation outgrows what $\pi$ can lower, and memory, which persists records across sessions. Both are transformations of state that produce state, and both therefore raise a question the body's partition answers only if one more closure property is stated: **provenance is a property of the information, not of its position in the pipeline — a transformation's output inherits the meet, in the trusted-writer lattice, of its inputs' labels.** Without that closure, compaction is a laundering channel: a summary of a session that contained an injected page can assert "the user asked to export the database," and the structural-intent check then validates future proposals against a plan the adversary bent — not through $\gamma$, not through $\rho$'s fold of a single $e$, but through the summarizer, which is a learned kernel (it lives in $M_W$, by the standing rule) and so cannot be trusted to preserve a partition it does not know exists. The discipline: summaries of data are data; the control-determining coordinates — plan, grants, what is authorized next — cross a compaction *verbatim* (copied, not paraphrased) or by re-confirmation from the trusted principal — never through the *summarizer*; the model rewrites the plan at plan steps, through the gated fold the body prices, and compaction is not one of them. Memory obeys the same closure twice, at write and at retrieval: the label rides the stored record across sessions, or a poisoned memory is an injection with an arbitrarily long fuse — and retrieval, being learned ($\pi$'s selection factor — adequacy-only behind the never-lower filter), decides what comes back but never what it is trusted *as*. The same test applies at birth: tool catalogs and server-supplied tool descriptions are third-party durable data that arrive dressed as instructions, and the lattice files them on the data side of $s_0$.
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-One more read-off, this time from irreversibility. *Destructive* compaction — dropping the original transcript once the summary is written — is a side effect against your own state that no later step can undo, and the gate-placement rule ("anything irreversible must be gated at authorization") does not exempt self-directed effects. The granularity preference then says what it said about bash: prefer the instrumented form — originals kept content-addressed, the summary an index and a cache rather than an authority, re-derivable when the $\pi$-sufficiency probe (*How this could be wrong*) says the summary dropped what mattered. A summary you can audit against its source is a lowering; a summary that replaced its source is a fait accompli.
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-**Composition (harness trees).** The cancellation entry already walked a tree — cancel flowing down, drains flowing up — and "a bash invocation that may itself be a harness" has hovered since the disposition trinary; what is missing is only the statement that makes both ordinary. From the parent's seat, a child harness *is* a $Q_E$ component: spawning it is an action authorized by $\gamma$ like any other, and the entire child run — its own $\pi, \gamma, \rho$, its own coins, its own halt — is one environment draw whose response $e$ is the child's terminal ledger. The law is four correspondences. The child's halting time is the parent's per-step *cost*: a parent certificate consumes a bound on $\mathbb{E}[\tau_H^{\mathrm{child}}]$ — the budget handed down at spawn, which the child's own budget-counter certificate discharges — or the parent's drift is uncontrolled however good its own $\hat V$. The child's ledger is the parent's *effect record*: the child's $e$ carries the $\mathsf{committed}/\mathsf{none}/\mathsf{unknown}$ accounting upward — which is what already let the cancellation entry make compensation the owner's job; the interface was this all along. And the child's non-accepting halts are the parent's *partial failures*: a refused child folds back as a response the parent routes around, not an exception that unwinds it. And the child's admissible effects are the parent's *$\Pi$-restriction*: the spawn grant bounds what the child can reach — the ledger reports what *happened*, the grant bounds what *could* — which is how safety composes without the parent ever reading the child's gate; the attenuation below is this correspondence stated as a rule. Read this way, the gate-granularity discipline and the tree are one preference: an instrumented child — budgeted, ledgered, cancellable — *is* the bounded, cancellable $Q_E$ the trinary prefers, and an opaque bash invocation is an un-annotated child you declined to instrument. Nesting adds no primitive on the environment side either: the parent never sees the child's gate and does not need to — it gates the spawn, prices the budget, folds the ledger, and the child's internal guarantees surface only as the shape of $e$. Nothing fixes one level: the tree recurses, budgets subdivide, ledgers concatenate upward, and the cooperative drain of cancellation is this law read under a cancel signal.
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-The tree leaves one seat unassigned: who plays trusted principal for a *child*? The parent — but with derived authority, not original, and the derivation is the narrow-only rule read along the spawn edge: **authority attenuates monotonically down the tree.** A spawn may grant the child any subset of the parent's own grants and nothing outside them; budgets subdivide, scopes narrow, and no edge widens. When a child asks-the-owner, the parent may answer from authority it already holds — that is attenuation working as designed — but a request beyond the parent's grants routes *up*, ultimately to the root principal, because a parent improvising an answer it was never granted is a learned kernel widening authorization: precisely what the gate-placement rule forbids a judge, and being a parent confers no exemption. The corollary is worth one sentence: a fully autonomous run is one whose root principal is unreachable, so the tree's only widening channel is closed and authorization is frozen at launch — not a limitation of the formalism but the honest price of the word *autonomous*.
-
-The pattern generalizes, and that is the point of the appendix. Nothing here added a primitive: the cancel is a signal in $s$, the gate closes by the rule it already follows, the in-flight disposition is forced by irreversibility, $H_{\mathrm{cancel}}$ is a subclass of an existing terminal set, and compensation is an ordinary owner-issued action — and the later entries kept the promise: resume re-enters $T$ at a persisted $s$, the batch gate was always in $\gamma$'s domain, provenance closure is the lattice's meet, attenuation is narrow-only read along an edge, and per-action capability is the gate's decision made enforceable. Every practical concern that earns a place here should resolve the same way — not new machinery, but the discipline the existing objects already imply, made explicit. Cancellation and resume, gate placement and the batch gate, effect records and the state derived from them, composition and delegation — those are the worked instances; the rest of the model is the same exercise.
-
-
----
-
-*The ramblings of Claude and Patrick.*

QUICKSTART.md

@@ -1,107 +1,92 @@
-# Quickstart
+# Bootstrap Wizard
 
-Install Turnstone, then diagnose it with `turnstone-doctor` if anything looks off.
+Interactive, AI-guided setup for Turnstone deployments. Instead of manually
+editing `.env` files and reading deployment docs, the wizard walks you through
+every decision conversationally and generates all the config files for you.
 
-## Install
-
-The one-line installer autodetects your distro (Ubuntu/Debian, Fedora/RHEL,
-Arch, and WSL), installs git + Docker if missing, generates secrets, picks free
-ports, and starts the stack:
+## Quick Start
 
 ```bash
-curl -fsSL https://raw.githubusercontent.com/turnstonelabs/turnstone/main/run.sh | bash
+turnstone-bootstrap
 ```
 
-Re-running is safe — it updates the checkout and keeps your existing `.env`.
-When it finishes it prints the dashboard URL and how to create the first admin
-user.
+That's it — no flags, no arguments. The wizard prompts for everything.
 
-**Other ways to install**
+## How It Works
 
-- **Already have Docker?** Clone the repo and `docker compose up` for the full
-  local cluster, or `docker compose -f turnstone/deploy/compose.yaml up` for the
-  released single-node stack. See [docs/docker.md](docs/docker.md).
-- **Python package:** `pip install turnstone` (add `--pre` for the experimental
-  track), then run `turnstone-server` / `turnstone-console` directly. See the
-  [README](README.md#quickstart).
+1. **Pick a model** — Choose OpenAI, Anthropic, or a local/vLLM endpoint to
+   power the wizard. Local endpoints auto-detect available models.
+2. **Answer questions** — The AI walks you through deployment mode, LLM
+   provider, database, authentication, ports, and optional features.
+3. **Review generated files** — Each file is previewed before writing. You
+   confirm or reject every write.
+4. **Start the stack** — The wizard prints the exact `docker compose` command
+   and a `setup.sh` script to create your first admin user, roles, and policies.
 
-## Diagnose: `turnstone-doctor`
+## What Gets Generated
 
-`turnstone-doctor` is an LLM-backed assistant that inspects a **running**
-Turnstone install and helps you troubleshoot it. It is **read-only** — it
-investigates and tells you the exact commands to fix things, but never changes
-your system. (Installation is the installer's job, not the doctor's.)
-
-```bash
-# From a host that has the turnstone package installed:
-turnstone-doctor
-
-# For a Docker install from run.sh (no package on the host), run it with pipx:
-pipx run --spec turnstone turnstone-doctor --dir ~/turnstone
-```
-
-### What it does
-
-1. **Preflight** — detects how Turnstone is installed here (docker-compose,
-   systemd/bare-metal, pip, or a source checkout) by probing for `config.toml`
-   files, `TURNSTONE_*` environment variables, compose files, and systemd units.
-2. **Self-configures its LLM** — it powers its own brain from your cluster's
-   *own* model configuration (env / `config.toml` / the database). Whether that
-   works is the first diagnostic: success means your LLM backend is healthy; if
-   it can't, that's surfaced as finding #1 and it falls back to asking you for a
-   provider and key so it can still help.
-3. **Version check** — reports the installed version, version drift across your
-   cluster's nodes, and the latest upstream stable/experimental releases.
-4. **Interactive diagnosis** — it reads logs, `/health`, `docker compose ps`,
-   `systemctl`, config, and ports to pin down problems like a node not joining
-   the console, an unreachable database, a down model backend, port conflicts,
-   or a JWT-secret mismatch — then hands you the precise remediation commands.
-
-### Flags
-
-| Flag | Purpose |
+| File | Purpose |
 |------|---------|
-| `--dir PATH` | Install directory to inspect (default: current directory) |
-| `--report` | Print the deterministic preflight report and exit — no LLM key needed |
-| `--offline` | Skip the upstream GitHub version check |
+| `.env` | All environment variables for `compose.yaml` |
+| `setup.sh` | Post-start script: creates admin user, roles, tool policies, prompt templates via the API |
+| `docker-compose.override.yaml` | Only if customizations beyond env vars are needed |
 
-`--report` is the fastest way to get a health snapshot (and to share one when
-asking for help) — it never needs an API key:
+## Requirements
 
-```bash
-turnstone-doctor --report --dir ~/turnstone
-```
+- **Python 3.11+** with turnstone installed (`pip install turnstone`)
+- **An LLM API key** — for the wizard itself (OpenAI, Anthropic, or a local
+  model). This can differ from the LLM your deployment will use.
+- **Docker & Docker Compose** — needed to run the stack. The wizard detects
+  whether Docker is installed and gives platform-specific install instructions
+  if it's missing. You can still generate config files without Docker.
+
+## Deployment Modes
+
+- **Single-node production** — `docker compose up` against the bundled
+  `turnstone/deploy/compose.yaml`: 1 server + console + channel + PostgreSQL,
+  pulled from ghcr.io. Good for most deployments.
+- **Local multi-node cluster** — clone the repo and run `docker compose up` at
+  the root for a 10-node fleet + console + Caddy + channel, built locally.
+
+See [docs/docker.md](docs/docker.md) for both.
+
+## Example Session
 
 ```
-## Install profile
-- Detected kind(s): docker-compose  (primary: docker-compose)
-- Docker daemon reachable: yes
-- Compose files:
-    /home/you/turnstone/compose.yaml
-- Database: backend=postgresql, url=postgresql+psycopg://turnstone:****@postgres:5432/turnstone
-- Candidate health URLs: http://localhost:8080/health, http://localhost:8090/health
+$ turnstone-bootstrap
 
-## Versions
-- Installed (this tool): 1.7.0a2
-- Cluster nodes: 10 reporting; versions ['1.7.0a2']
-- Version drift across nodes: no
-- Upstream: stable 1.6.9, experimental 1.7.0a2
+ Turnstone Bootstrap Wizard  v1.5.0
+ ────────────────────────────────────────────────
 
-## LLM backend (ok)
-- resolved Qwen/Qwen3-32B via openai-compatible @ http://host.docker.internal:8000/v1
+ Which provider for this wizard?
+   [1] OpenAI
+   [2] Anthropic
+   [3] OpenAI-compatible (local/vLLM)
+
+ > 3
+
+ Base URL [http://localhost:8000/v1]:
+ API key (press Enter for 'none'):
+
+ Querying http://localhost:8000/v1 for available models...
+ Found model: Qwen/Qwen3-32B
+
+ Connected to Qwen/Qwen3-32B. Handing off to AI assistant...
+
+> (AI walks you through the rest interactively)
 ```
 
-Secrets (JWT secret, database password, API keys) are always redacted in the
-report and in anything the doctor reads.
-
 ## Tips
 
-- **Type `quit`** to exit the conversation; **Ctrl+C** interrupts (twice to quit).
-- **Point it at the right install** with `--dir` when you run it from elsewhere.
-- **(Re)installing or adding nodes?** Use the installer (`run.sh`), not the doctor.
+- **Re-run safely** — running the wizard again detects your existing `.env`
+  and offers to update it rather than overwriting.
+- **Duplicate writes are skipped** — if the LLM tries to write the same file
+  twice with identical content, it's silently ignored.
+- **Type `quit` to exit** at any time during the conversation.
+- **Ctrl+C** is handled gracefully — press once to interrupt, twice to exit.
 
 ## See Also
 
-- [Docker Deployment](docs/docker.md) — compose stacks, ports, and bare-metal nodes
+- [Docker Deployment](docs/docker.md) — manual compose setup and profiles
 - [Security](docs/security.md) — auth architecture and token types
 - [Governance](docs/governance.md) — roles, policies, and templates

README.md

@@ -14,14 +14,6 @@ Self-hosted, local-first orchestration for tool-using AI agents. Give LLMs real
 
 Named after the [Ruddy Turnstone](https://en.wikipedia.org/wiki/Ruddy_turnstone) (*Arenaria interpres*) — a shorebird that flips stones to discover what's hiding underneath.
 
-**What is a harness?**
-
-```
-ℋ :  s_{n+1} ~ T(s_n)   for n < τ*,    T = ρ ∘ (M_W ∘ π, E)
-```
-
-[**the hypothesis →**](HYPOTHESIS.md)
-
 ### Release Tracks
 
 | Track | Install | Docker | Description |
@@ -35,7 +27,7 @@ See [docs/releasing.md](docs/releasing.md) for the full release process.
 
 Turnstone gives LLMs tools — shell, files, search, web, planning — and orchestrates multi-turn conversations where the model investigates, acts, and reports.
 
-- **Local-first & private** — runs entirely on hardware you control, with no telemetry and no phone-home. Point it at local models (vLLM, llama.cpp) or commercial APIs you hold the keys to — your prompts and data never transit a third party you didn't choose.
+- **Local-first & private** — runs entirely on hardware you control, with no telemetry and no phone-home. Point it at local models (vLLM, llama.cpp, Ollama) or commercial APIs you hold the keys to — your prompts and data never transit a third party you didn't choose.
 - **Bring your own models** — OpenAI-compatible APIs (vLLM, llama.cpp, NIM), the Anthropic Messages API, and Google Gemini, mixed freely per role
 - **Interactive sessions** — terminal CLI or browser UI with parallel workstreams
 - **Cluster dashboard** — real-time view of every node and workstream, with a rendezvous routing proxy
@@ -94,7 +86,7 @@ LLM; add model backends from the console UI.
 For production (released images from ghcr.io, real secrets required), use the
 bundled stack: `docker compose -f turnstone/deploy/compose.yaml up`.
 
-See [QUICKSTART.md](QUICKSTART.md) for the install + troubleshooting walkthrough and [docs/docker.md](docs/docker.md) for Docker configuration.
+See [QUICKSTART.md](QUICKSTART.md) for the bootstrap wizard and [docs/docker.md](docs/docker.md) for Docker configuration.
 
 ### Programmatic (SDK)
 
@@ -124,9 +116,8 @@ Built-in tools for shell, files, search, web, memory, notifications, and autonom
 | `turnstone-console` | Cluster dashboard + routing proxy + admin panel |
 | `turnstone-channel` | Channel gateway (Discord and Slack adapters) |
 | `turnstone-admin` | User/token management CLI |
-| `turnstone-eval` | Headless measurement — scores tool-use against expected actions |
-| `turnstone-optimizer` | Prompt/tool optimizer (UCB self-modify loop over the eval substrate) |
-| `turnstone-doctor` | LLM-backed cluster diagnostics |
+| `turnstone-eval` | Eval harness for prompt/tool optimization |
+| `turnstone-bootstrap` | LLM-guided setup wizard |
 
 ### Diagrams
 

docs/api-reference.md

@@ -698,42 +698,6 @@ Each skill summary:
 
 ---
 
-### `GET /v1/api/personas`
-
-Returns the enabled personas offered by the workstream-creation pickers.
-Authenticated for any logged-in user and deliberately gated by **no**
-`persona.*` permission — selecting a persona at creation is a user
-action, while the `persona.*` perms gate authoring. Display fields only;
-the levers (base prompt, tool set, MCP/memory toggles) stay server-side.
-
-**Response:**
-
-```json
-{
-  "personas": [
-    {"name": "engineer", "display_name": "Engineer", "description": "The stock interactive workstream: full tools, MCP, and memory.", "applies_to_kinds": ["interactive"], "is_default": true},
-    {"name": "researcher", "display_name": "Researcher", "description": "Answers questions with evidence — reads and cites, loads tools to verify when needed.", "applies_to_kinds": ["interactive"], "is_default": false}
-  ],
-  "total": 2
-}
-```
-
-Each persona summary:
-
-| Field              | Type   | Description                                                      |
-|--------------------|--------|------------------------------------------------------------------|
-| `name`             | string | Persona slug (used in the `persona` field on workstream creation) |
-| `display_name`     | string | Human-readable label for pickers                                 |
-| `description`      | string | Short description of the persona's intent                        |
-| `applies_to_kinds` | array  | Workstream kinds the persona applies to (`interactive` / `coordinator`) |
-| `is_default`       | bool   | Whether this is the default persona for its kind                 |
-
-> **Note:** For full persona management (create, edit, archive), use the
-> admin endpoints at `/v1/api/admin/personas` (requires the
-> `persona.{create,read,write}` permissions).
-
----
-
 ### `POST /v1/api/workstreams/{ws_id}/send`
 
 Sends a user message to a workstream. Spawns a daemon worker thread that calls
@@ -931,7 +895,6 @@ All fields are optional. The body can be empty or an empty JSON object.
 | `auto_approve`   | bool   | false   | Auto-approve all tool calls for this workstream                |
 | `resume_ws`      | string | ""      | Workstream ID to resume atomically during creation (empty = fresh)|
 | `skill`          | string | ""      | Skill name. Applies content (system prompt), model, temperature, reasoning effort, max tokens, auto-approve policy, token budget, and other session config from the skill. Returns 400 if not found or disabled. Ignored when `resume_ws` is set (resumed sessions restore their own skill). |
-| `persona`        | string | ""      | Persona slug. Resolved and snapshotted into the workstream at creation; empty selects the kind's default. |
 | `judge_model`    | string | ""      | Optional model alias for the judge (overrides default judge model for this workstream) |
 
 > **Skill behavior:** When `skill` is specified, the skill's content is injected as a system message and its session config fields (model, temperature, auto-approve, token budget, etc.) override system defaults for the new workstream.

docs/architecture.md

@@ -19,11 +19,10 @@ plugs in.
 | `turnstone` | `turnstone.cli` | `TerminalUI` | Interactive terminal REPL |
 | `turnstone-server` | `turnstone.server` | `WebUI` | Browser-based chat (HTTP + SSE) |
 | `turnstone-console` | `turnstone.console.server` | ClusterCollector | Cluster dashboard (aggregates all nodes) |
-| `turnstone-eval` | `turnstone.eval.cli` | `NullUI` | Headless measurement (scores tool-use against expected actions) |
-| `turnstone-optimizer` | `turnstone.optimizer` | `NullUI` | Prompt/tool optimization (UCB self-modify loop over the eval substrate) |
+| `turnstone-eval` | `turnstone.eval` | `NullUI` | Headless evaluation and prompt optimization |
 | `turnstone-channel` | `turnstone.channels.cli` | ChannelAdapter | Channel gateway (Discord, Slack, etc.) |
 | `turnstone-admin` | `turnstone.admin` | — | Offline user and API token management |
-| `turnstone-doctor` | `turnstone.doctor` | — | LLM-backed cluster diagnostics |
+| `turnstone-bootstrap` | `turnstone.bootstrap` | — | LLM-guided setup wizard |
 
 ---
 
@@ -268,7 +267,7 @@ the per-workstream events stream in
 |-------|--------|-------|
 | `TerminalUI` | `turnstone.cli` | ANSI colors, `MarkdownRenderer`, `Spinner`, readline-based `input()` for approval |
 | `WebUI` | `turnstone.server` | SSE event queue per workstream + global broadcast, `threading.Event` for blocking on approval. `on_state_change` sends to both per-workstream and global SSE (the browser UI uses per-workstream `state_change` events to manage busy/idle transitions; `stream_end` only finalizes markdown rendering). |
-| `NullUI` | `turnstone.eval.core` | Discards all output; `approve_tools` always returns `(True, None)` |
+| `NullUI` | `turnstone.eval` | Discards all output; `approve_tools` always returns `(True, None)` |
 
 ### WorkstreamTerminalUI
 
@@ -634,17 +633,8 @@ function tool (the model always searches). Citations from `url_citation`
 annotations are formatted as footnotes. Extended prompt cache retention
 (`prompt_cache_retention: "24h"`) is enabled for GPT-5.x models at no
 additional cost. Cached token counts are extracted from
-`usage.prompt_tokens_details.cached_tokens`. Unknown models get permissive
-defaults with `supports_vision=False` and use SearxNG for web search. The
-`openai-compatible` lane never consults this table at all — on either API
-surface (the responses pin is served by a compat-mode
-`OpenAIResponsesProvider`, mirroring `AnthropicProvider(compat=True)`): a
-local server serves whatever the operator named it (vLLM
-`--served-model-name` is a free string), so a prefix collision with a cloud
-model id must not inherit that model's sampling/effort contract — every
-local model gets the plain defaults, and anything beyond them is declared on
-the model definition (capabilities JSON + `server_compat`), matching the
-`anthropic-compatible` lane.
+`usage.prompt_tokens_details.cached_tokens`. Unknown models (local servers) get
+permissive defaults with `supports_vision=False` and use SearxNG for web search.
 
 **AnthropicProvider** (`_anthropic.py`): converts OpenAI-format messages to
 Anthropic content blocks, maps `system`/`developer` roles to the `system`
@@ -807,105 +797,15 @@ model = "deepseek-ai/DeepSeek-V4-Flash"
 supports_vision = true                    # multimodal checkpoints only
 supports_mid_conversation_system = true   # template-dependent
 context_window = 131072
-thinking_mode = "manual"                  # session effort knob drives the template toggle
-thinking_param = "enable_thinking"        # Qwen/Gemma key; "thinking" for Granite/DeepSeek
 ```
 
-Reasoning control does NOT use Anthropic's `thinking` request param —
-the levers live in the chat template, reached through
-`chat_template_kwargs` in the request body. Two channels, dynamic first:
-
-* **Session effort knob (dynamic).** Set the model's thinking mode to
-  "Effort-knob controlled" in the admin Models form (or
-  `thinking_mode = "manual"` + `thinking_param` under
-  `[models.*.capabilities]`) and the provider maps the session's
-  reasoning-effort knob onto the template toggle per-request: effort
-  `none` sends `{<thinking_param>: false}`, any other level sends
-  `true` — the same contract as the real lane's manual mode. ("Always
-  on" / `thinking_mode = "adaptive"` instead always sends `true`: the
-  model self-regulates, so the knob never force-disables — mirroring
-  the native adaptive branch.) The graded effort value always rides
-  alongside the toggle: under `effort_param` when the operator names
-  the template's key, else under the conventional fallback key
-  (`reasoning_effort`) on the anthropic-compatible lane — the user's
-  effort setting always reaches the wire, and a template that doesn't
-  reference the kwarg ignores it. On the openai-compatible lane the
-  undeclared-key case rides the flat top-level `reasoning_effort`
-  param instead (the documented compat field), forwarded verbatim.
-  Optional `reasoning_effort_values` / `default_reasoning_effort`
-  validate the knob before it reaches the server; without declared
-  values the knob is forwarded as-is. The knob is ordinal, and validation
-  respects that: an off-list knob value rounds UP onto the declared
-  list and a value above the ceiling rides the ceiling
-  (`snap_reasoning_effort`) — asking for more effort than the model
-  declares never falls back to a lower default tier.  The knob's
-  `none` position is forwarded verbatim when the model declares an
-  explicit `none` level (gpt-5.1+, grok-4.3) — omitting it there would
-  leave a reasoning-on server default (e.g. gpt-5.5's `medium`) in
-  charge of a knob that promises off — and omitted otherwise; `none`
-  is never a snap target for other positions.
-  `default_reasoning_effort` only catches values the ordinal snap
-  cannot rank (custom strings). Declare values that match the
-  template's documented vocabulary: for DeepSeek-V4, which officially
-  accepts `high`/`max` (Think High is the default thinking tier;
-  `low`/`medium` alias to `high`, `xhigh` to `max`), a
-  `("high", "max")` values list reproduces the official aliasing
-  exactly — `low`/`medium` round up to `high`, `xhigh` to `max` —
-  and freeform passthrough matches it too. To map an undocumented
-  template, probe with per-request `chat_template_kwargs` and compare
-  `input_tokens`. Setting `effort_param` also suppresses the
-  flat top-level `reasoning_effort` request param on the
-  openai-compatible lane — the template channel replaces it, never
-  doubles it. With the default `thinking_mode = "none"` nothing is
-  injected and the server's template default decides.
-
-  Upgrade note: before 1.7.0a7 the openai-compatible lane sent the
-  toggle unconditionally `true` whenever thinking mode was enabled. A
-  stored per-model `reasoning_effort = "none"` now disables thinking
-  on such models — pick any real level (or clear the override) to keep
-  it on. Also since 1.7.0a7 the effort level itself always reaches the
-  wire on the local lanes (previously dropped unless
-  `reasoning_effort_values` was declared): flat `reasoning_effort` on
-  openai-compatible, the `effort_param`-or-fallback template key on
-  anthropic-compatible when reasoning control is engaged.
-* **Operator pin (static).** Entries under `{"chat_template_kwargs":
-  ...}` in the admin Models extra-body field ride the SDK's
-  `extra_body` unconditionally and win over the knob mapping on key
-  collision — e.g. pin `{"enable_thinking": true}` to keep thinking on
-  regardless of the session knob. (Server type and API surface remain
-  openai-compatible-only knobs and stay hidden for this provider.)
-
-The same knob mapping drives the `openai-compatible` lane's Chat
-Completions requests — `merge_reasoning_template_kwargs` is shared by
-both local-server lanes, so `thinking_mode`/`thinking_param`/
-`effort_param` mean the same thing whichever endpoint serves the model.
-Only the Responses API surface (native reasoning) ignores it.
-
-The console surfaces this projection as an *effective effort ladder*:
-the admin model form's per-model effort select and the skill
-launch-config effort select annotate each position with what the
-request will carry, in plain words — a position whose delivered level
-matches its name stays plain ("Max"), a snapped position says so
-("Low — sends high"), the adaptive lanes' none position warns
-"thinking stays on", and budget detail lives in the tooltip. A
-position is never labeled after a sibling that shares its wire (that
-rendered "Max (= minimal)", implying a downgrade the wire doesn't
-contain). Computed server-side by `providers/effort_ladder.py` from
-the same mapping functions the providers use at request time and
-shipped on `/v1/api/models` rows (every row carries `effort_ladder`,
-empty when the capabilities column fails to parse) and
-`POST /v1/api/admin/models/effort-ladder`. The ladder describes what
-Turnstone sends — a server-side template may alias further (DeepSeek-V4
-folds `low`/`medium` into its default `high` tier).
-
-The `anthropic-compatible` lane never sends Anthropic's native
-`thinking`/`output_config` params — they are not in vLLM's request
-schema. The real `anthropic` provider is unaffected: official Claude
-models keep native thinking, budget mapping, and `output_config`
-effort. A gateway fronting *real* Claude on a Messages-shaped URL
-(e.g. a LiteLLM `anthropic/` route to the Claude API) should use
-`provider = "anthropic"` with a custom `base_url`, which keeps the
-native thinking params.
+The reasoning toggle does NOT use Anthropic's `thinking` request param.
+Toggle it through the chat template instead: set `{"chat_template_kwargs":
+{"thinking": false}}` as extra body params in the admin Models
+server-compat section (for this provider the section shows only the
+extra-body field — server type, API surface, and thinking mode are
+openai-compatible-only knobs); the provider forwards it via the SDK's
+`extra_body`.
 
 Verified quirks of vLLM's Anthropic endpoint:
 
@@ -1117,10 +1017,9 @@ reconstructs the OpenAI message format from database rows:
   in the same workstream
 
 **Config persistence:** LLM-affecting parameters (`temperature`,
-`reasoning_effort`, `max_tokens`, `instructions`, and the persona
-snapshot — see `docs/personas.md`) are persisted to the
-`workstream_config` table on creation and whenever changed via slash
-commands. `resume()` restores these values so resumed workstreams
+`reasoning_effort`, `max_tokens`, `instructions`, `creative_mode`) are
+persisted to the `workstream_config` table on creation and whenever changed
+via slash commands. `resume()` restores these values so resumed workstreams
 behave identically to the original.
 
 **`/clear` vs `/new`:** `/clear` wipes in-memory context but preserves

docs/bulk-endpoints.md

@@ -12,6 +12,7 @@ Existing bulk endpoints at time of writing:
 |---------------------------------------------------------|--------------------------|------------------------------------------|
 | `GET  /v1/api/cluster/ws/live?ids=a,b,c`                | bulk read                | `{results, denied, truncated}`           |
 | model tool `spawn_batch`                                 | bulk create (per-item)   | `{results, denied}`                      |
+| `POST /v1/api/workstreams/{ws_id}/stop_cascade`          | cascade mutation         | `{cancelled, failed, skipped}`           |
 | `POST /v1/api/workstreams/{ws_id}/close_all_children`    | cascade mutation         | `{closed, failed, skipped}`              |
 
 ---
@@ -145,7 +146,7 @@ consistently-typed across the read and create cases.
 ```
 
 Where `<bucket>` is the endpoint-specific name for "succeeded" —
-`closed` for `close_all_children`.
+`cancelled` for `stop_cascade`, `closed` for `close_all_children`.
 The three buckets partition the input set exactly once:
 
 | Bucket        | Meaning                                                                       |
@@ -160,6 +161,20 @@ be partial.  `skipped` is pre-resolved — the target is already in
 the terminal state the cascade was aiming at, so it's neither a
 win to report nor a fault to fix.
 
+### Example — `stop_cascade`
+
+```json
+{
+  "status": "ok",
+  "cancelled": ["child-1", "child-3"],
+  "failed":    [],
+  "skipped":   ["child-2"]
+}
+```
+
+A subsequent retry would target only `failed` ids, not `skipped`
+ones — the latter are already done.
+
 ### Example — `close_all_children`
 
 ```json
@@ -171,12 +186,10 @@ win to report nor a fault to fix.
 }
 ```
 
-Here the success bucket is `closed`.  A subsequent retry would
-target only `failed` ids, not `skipped` ones — the latter are
-already done.  When `coord_client` is unavailable (session loaded
-but no HTTP client attached — a construction bug) every id goes to
-`failed` so the operator notices rather than getting a silent
-all-skipped response.
+Same partition, different success-bucket name.  When `coord_client`
+is unavailable (session loaded but no HTTP client attached — a
+construction bug) every id goes to `failed` so the operator notices
+rather than getting a silent all-skipped response.
 
 ---
 
@@ -219,12 +232,12 @@ all-skipped response.
 
 - **Phase 6** shipped `cluster/ws/live` as the first Shape A endpoint
   (`{results, denied, truncated}`).
-- **Phase 7** introduced the Shape B cascade-mutation envelope
-  (`{<bucket>, failed, skipped}`) for the coordinator's
-  cancel-cascade path.
+- **Phase 7** shipped `stop_cascade` as the first Shape B endpoint
+  (`{cancelled, failed, skipped}`).
 - **Phase 8 PR A** shipped `spawn_batch` (Shape A, keyed by idx) and
-  `close_all_children` (Shape B), which crystallised the
-  two-shape-per-semantic-category policy codified here.
+  `close_all_children` (Shape B, twin of `stop_cascade`), which
+  crystallised the two-shape-per-semantic-category policy codified
+  here.
 
 Before adding a third shape, read this doc and argue for why the
 new surface doesn't fit either A or B.  Two idioms in the cluster

docs/console.md

@@ -379,7 +379,6 @@ Breadcrumb: `Cluster > Running` or `Cluster > db-west-04`. Server-side paginated
 Triggered by the "+ new" header button. A modal dialog with:
 
 - **Node selector** — dropdown with three targeting modes: "Auto (best available)" picks the node with the most headroom, "General pool (any node)" picks a node with available capacity using round-robin, or a specific node from the list (showing capacity).
-- **Persona** — optional dropdown listing the enabled personas for the workstream kind. Sets the system-message composition and capability envelope at creation, snapshotted server-side; empty uses the kind's default. Picking one requires no `persona.*` permission.
 - **Profile** — optional dropdown listing enabled skills. Applies the skill's model, auto-approve policy, token budget, and other behavioral settings at creation time.
 - **Name** — optional text input. Auto-generated if left empty.
 - **Model** — optional text input for a model alias from the target node's registry.
@@ -397,9 +396,9 @@ The browser maintains a local `clusterState` object that mirrors the cluster sna
 
 Accessed via the "admin" button in the header (visible when authenticated
 with `approve` scope). Provides user, API token, channel link, MCP server,
-and skill management with tabs that include Users, API Tokens, Channels,
-Schedules, Watches, Personas, Roles, Policies, Prompts, Judge, Skills,
-MCP Servers, Usage, Audit, Memories, Models, Nodes, Settings, and TLS.  See also
+and skill management with 18 tabs (Users, API Tokens, Channels, Schedules,
+Watches, Roles, Policies, Prompts, Judge, Skills, MCP Servers, Usage,
+Audit, Memories, Models, Nodes, Settings, TLS).  See also
 [Governance](governance.md) for the Roles, Policies, Skills, Usage, and
 Audit tabs, and [Settings](settings.md) for the database-backed
 configuration editor.

docs/coordinator-api-tour.md

@@ -18,7 +18,7 @@ schema changes.
 > auth and the `admin.coordinator` permission.  A session-scoped JWT
 > is minted per login (see [docs/oidc.md](oidc.md) / [docs/security.md](security.md));
 > a service token may call the read paths but destructive governance
-> paths (`/restrict`, `/close_all_children`) require
+> paths (`/restrict`, `/stop_cascade`, `/close_all_children`) require
 > the explicit `admin.coordinator` grant — a service-token owner
 > match isn't enough.
 
@@ -44,6 +44,7 @@ schema changes.
 | 6 | Wait for fan-out             | model-side tool `wait_for_workstream`                       |
 | 7 | Govern                       | `POST /v1/api/workstreams/{ws_id}/trust`                    |
 |   |                              | `POST /v1/api/workstreams/{ws_id}/restrict`                 |
+|   |                              | `POST /v1/api/workstreams/{ws_id}/stop_cascade`             |
 |   |                              | `POST /v1/api/workstreams/{ws_id}/close_all_children`       |
 | 8 | Approve / cancel             | `POST /v1/api/workstreams/{ws_id}/approve`                  |
 |   |                              | `POST /v1/api/workstreams/{ws_id}/cancel`                   |
@@ -52,7 +53,7 @@ schema changes.
 Refer to `/openapi.json` (Swagger UI at `/docs`) on any
 `turnstone-console` process for the authoritative operation ids and
 schemas. Coordinator-only verbs (`/children`, `/trust`, `/restrict`,
-`/close_all_children`) 404 against `kind=interactive`
+`/stop_cascade`, `/close_all_children`) 404 against `kind=interactive`
 rows; the shared verbs (`/send`, `/approve`, `/cancel`, `/events`,
 `/history`, `/open`, `/close`, etc.) work on both kinds.
 
@@ -260,10 +261,10 @@ rounds to a 10× token-efficiency win.
 
 ---
 
-## 7. Governance — trust, restrict, close_all_children
+## 7. Governance — trust, restrict, stop_cascade, close_all_children
 
-These three endpoints let an operator steer a live coordinator session
-mid-flight.  All three emit an audit event tagged
+These four endpoints let an operator steer a live coordinator session
+mid-flight.  All four emit an audit event tagged
 `coordinator.<action>` via the dedicated audit executor so a cascade
 burst can't starve audit writes.
 
@@ -293,6 +294,28 @@ idempotent — calling twice with overlapping lists converges to the
 union.  Revocations don't survive a session close/reopen; operators
 opt in per session.  Cap 256 tool names per request, 128 chars each.
 
+### `POST /stop_cascade` — cancel the subtree
+
+```http
+POST /v1/api/workstreams/{ws_id}/stop_cascade
+{}
+```
+
+Cancels the coordinator's in-flight generation AND dispatches
+`cancel_workstream` through the routing proxy for every direct
+child in the in-memory registry.  Returns:
+
+```json
+{"status": "ok", "cancelled": ["child-1", "child-3"], "failed": [], "skipped": ["child-2"]}
+```
+
+Response uses the [cascade-mutation bulk shape](bulk-endpoints.md):
+`cancelled` = accepted, `failed` = dispatch error worth retrying,
+`skipped` = upstream 404 (already gone — stale registry entry or
+the row was deleted between snapshot and dispatch).  Grandchildren
+aren't touched directly; they sit behind their parent's cancel and
+propagate via the child's SSE stream.
+
 ### `POST /close_all_children` — soft-close the direct fan-out
 
 ```http
@@ -306,16 +329,16 @@ Response:
 {"status": "ok", "closed": ["c-1", "c-2"], "failed": [], "skipped": []}
 ```
 
-Soft-close cascade bounded by a concurrency semaphore.  The `reason`
-(up to 512 chars) propagates into each closed child's audit +
-`workstream_config` for postmortem.  The model-facing tool that
-pairs with this endpoint asks for a bounded teardown of the
-coordinator's own fan-out.  This *soft-closes*; to *cancel* the
-fan-out instead, cancel the coordinator (§8) — a coordinator cancel
-auto-cascades to its direct children.
+Soft-close cascade bounded by the same semaphore as `stop_cascade`.
+The `reason` (up to 512 chars) propagates into each closed child's
+audit + `workstream_config` for postmortem.  Unlike `stop_cascade`
+this does NOT recurse into grandchildren — the model-facing tool
+that pairs with this endpoint asks for a bounded teardown of the
+coordinator's own fan-out.  For a full-subtree teardown, use
+`stop_cascade`.
 
-See [bulk-endpoints.md](bulk-endpoints.md) for why `close_all_children`
-uses the cascade-mutation shape and how it differs from the
+See [bulk-endpoints.md](bulk-endpoints.md) for why both endpoints
+share the cascade-mutation shape and how it differs from the
 `spawn_batch` / `cluster/ws/live` shape.
 
 ---
@@ -333,10 +356,7 @@ POST /v1/api/workstreams/{ws_id}/approve
 {"approved": true, "feedback": null, "always": true}    // always-approve this tool name
 ```
 
-`cancel` drops the coordinator's in-flight generation and, for a
-coordinator, auto-cascades the cancel to its direct children:
-`cancel_workstream` is dispatched through the routing proxy for
-every direct child in the registry.  The coordinator itself is left
+`cancel` drops the in-flight generation but leaves the coordinator
 idle and open for a fresh `send`:
 
 ```http
@@ -353,10 +373,9 @@ POST /v1/api/workstreams/{ws_id}/close
 {}
 ```
 
-Soft-closes the session — state persists, children keep running
-(wind them down first with `close_all_children`, or by cancelling
-the coordinator, which cascades the cancel to its direct children),
-the worker thread exits, SSE streams send a final `stream_end` and
+Soft-closes the session — state persists, children keep running (use
+`close_all_children` or `stop_cascade` first to wind them down), the
+worker thread exits, SSE streams send a final `stream_end` and
 disconnect.  The row is reopenable via
 `POST /v1/api/workstreams/{ws_id}/open` so long as it hasn't been
 deleted.
@@ -366,12 +385,12 @@ deleted.
 ## Further reading
 
 - [coordinator-skills.md](coordinator-skills.md) — writing a skill
-  that runs on a coordinator session (orchestrator framing,
+  that runs on a coordinator session (orchestrator persona,
   workflow patterns, `SkillKind` classifier).
 - [bulk-endpoints.md](bulk-endpoints.md) — the two bulk-shape
   idioms (`{results, denied, truncated}` vs
   `{<bucket>, failed, skipped}`) used by `cluster/ws/live`,
-  `spawn_batch`, and `close_all_children`.
+  `spawn_batch`, `stop_cascade`, and `close_all_children`.
 - [architecture.md](architecture.md) — cluster-wide architecture
   including how coordinator sessions fit next to node-hosted
   interactive workstreams.

docs/coordinator-skills.md

@@ -1,13 +1,13 @@
 # Writing a coordinator-specific skill
 
-A skill is prompt-level framing that steers a Turnstone session
+Skills are prompt-level personas that steer a Turnstone session
 toward a narrow task.  Most skills target **interactive** sessions —
 the single-workstream "do this thing" surface where the model wields
 `bash`, `edit_file`, `web_fetch`, and the rest of the maker toolset.
 
 A **coordinator skill** is different.  It runs on a session whose job
 is to orchestrate other sessions.  The toolset is smaller and
-narrower, the role is an orchestrator instead of a maker, and the
+narrower, the persona is an orchestrator instead of a maker, and the
 success metric is "did the plan resolve" instead of "did the code
 compile".  This doc covers the differences a skill author has to
 care about.
@@ -22,8 +22,8 @@ migration 044 added the column).  Three values:
 
 | `SkillKind` enum        | Stored as       | Meaning                                                                    |
 |-------------------------|-----------------|----------------------------------------------------------------------------|
-| `SkillKind.INTERACTIVE` | `"interactive"` | Authored for the interactive maker role (single-workstream "do this"). |
-| `SkillKind.COORDINATOR` | `"coordinator"` | Authored for the orchestrator role (delegate, monitor, synthesise).    |
+| `SkillKind.INTERACTIVE` | `"interactive"` | Authored for the interactive maker persona (single-workstream "do this"). |
+| `SkillKind.COORDINATOR` | `"coordinator"` | Authored for the orchestrator persona (delegate, monitor, synthesise).    |
 | `SkillKind.ANY`         | `"any"`         | Either surface (or audience-neutral).  Default on create.                 |
 
 The `kind` field is a `StrEnum` — drop-in `str` compatible — so DB
@@ -96,20 +96,20 @@ for the output.  The coordinator stays the orchestrator.
 
 ---
 
-## Framing differences
+## Persona differences
 
 Interactive skills compose on top of `base_interactive.md` — a
-"maker" framing: get the work done, use the tools, edit the code,
+"maker" persona: get the work done, use the tools, edit the code,
 close the loop.
 
 Coordinator skills compose on top of
-[`personas/orchestrator.md`](../turnstone/prompts/personas/orchestrator.md) —
-an "orchestrator" framing: decompose, delegate, monitor, synthesise.
+[`base_coordinator.md`](../turnstone/prompts/base_coordinator.md) —
+an "orchestrator" persona: decompose, delegate, monitor, synthesise.
 The base text is short but sets the tone every coordinator skill
 inherits:
 
-> You are a coordinator.  Your role is to orchestrate work across
-> the cluster...  You do
+> You are a coordinator on a small, focused infrastructure team.
+> Your role is to orchestrate work across the cluster...  You do
 > not edit files, run shell commands, browse the web, or manipulate
 > the codebase directly.  Children do that.
 
@@ -339,7 +339,7 @@ For a new coordinator skill:
 A full end-to-end test isn't required for every skill; a
 prepare-step unit test that asserts "given this initial message, the
 first tool call is X with Y args" is usually sufficient to catch
-framing drift without a real LLM in the loop.
+persona drift without a real LLM in the loop.
 
 ---
 
@@ -351,7 +351,7 @@ framing drift without a real LLM in the loop.
   `spawn_batch` and `close_all_children` use, so your skill can
   parse results / denied arrays correctly.
 - [governance.md](governance.md) — the broader governance surface
-  (`/trust`, `/restrict`, role-based permissions)
+  (`/trust`, `/restrict`, `/stop_cascade`, role-based permissions)
   that wraps every coord session.
 - [settings.md](settings.md) — `coordinator.model_alias` and
   `coordinator.reasoning_effort` settings that gate which LLM runs

docs/docker.md

@@ -125,7 +125,7 @@ docker compose -f turnstone/deploy/compose.yaml up
 It's the same shape as the dev stack — Caddy-fronted console, channel, and a
 PostgreSQL all share one database so the console discovers the node — but it
 pulls released images, runs a single server node, and has **no baked-in
-secrets**. Set these in `.env` first (generate with `openssl rand -hex 32`):
+secrets**. Set these in `.env` first (`turnstone-bootstrap` generates them):
 
 ```bash
 TURNSTONE_JWT_SECRET=<python -c "import secrets; print(secrets.token_hex(32))">
@@ -260,7 +260,7 @@ interface, or anyone who can reach it can search through your instance.
 
 Both stacks install all entry points into a single image (`turnstone`,
 `turnstone-server`, `turnstone-console`, `turnstone-channel`, `turnstone-admin`,
-`turnstone-eval`, `turnstone-optimizer`, `turnstone-doctor`):
+`turnstone-eval`, `turnstone-bootstrap`):
 
 ```bash
 docker compose build            # build the dev image

docs/eval.md

@@ -1,19 +1,11 @@
-# Evaluation and Prompt Optimization (turnstone-eval, turnstone-optimizer)
+# Evaluation and Prompt Optimization (turnstone-eval)
 
-Evaluation for turnstone is split into two commands:
+`turnstone-eval` is the evaluation and prompt optimization system for turnstone. It
+runs test cases against the LLM, scores tool call sequences against expected
+actions, and optionally uses a multi-agent pipeline to optimize the developer
+prompt and tool descriptions.
 
-- **`turnstone-eval`** — the measurement substrate. Runs test cases against the LLM
-  and scores tool call sequences against expected actions. A single measurement pass,
-  no self-modification.
-- **`turnstone-optimizer`** — the prompt/tool optimizer. Loops over the measurement
-  substrate, using a multi-agent pipeline (analyst, optimizer, observer, diversifier,
-  tool optimizer) to edit the developer prompt and tool descriptions so more tests pass.
-
-The dependency is strictly one-way: the optimizer consumes the eval substrate; the
-substrate never depends on the optimizer.
-
-Source: `turnstone/eval/core.py` (measurement substrate), `turnstone/eval/cli.py`
-(the `turnstone-eval` CLI), `turnstone/optimizer.py` (the `turnstone-optimizer` CLI).
+Source: `turnstone/eval.py`
 
 ---
 
@@ -35,8 +27,8 @@ This approach (inspired by [Learning to Self-Evolve](https://arxiv.org/abs/2603.
 prevents irrecoverable collapse from bad edits — UCB naturally backtracks to
 high-scoring ancestors instead of following a linear chain.
 
-The `turnstone-eval` command (or `turnstone-optimizer --no-optimize`) executes only
-steps 2-4: a single measurement pass over the root prompt, no optimization.
+When optimization is disabled (`--no-optimize`), only steps 2-4 execute
+(a single iteration evaluating the root node).
 
 ---
 
@@ -460,46 +452,30 @@ structure is:
 
 ## CLI Usage
 
-Two console scripts (installed as entry points), or the equivalent `python -m`
-invocations:
-
-- `turnstone-eval` / `python -m turnstone.eval.cli` — measure only.
-- `turnstone-optimizer` / `python -m turnstone.optimizer` — optimize.
-
-### Measure (`turnstone-eval`)
+The entry point is `turnstone-eval` (installed as a console script) or
+`python -m turnstone.eval`.
 
 ```
-turnstone-eval tests.json                     # one measurement pass, print scores
-turnstone-eval tests.json --prompt custom.txt # measure a custom prompt
-turnstone-eval tests.json --n-runs 5          # more runs per case
-turnstone-eval tests.json --parallel 4        # run cases across 4 workers
-turnstone-eval tests.json -v                  # verbose per-turn logging
+turnstone-eval tests.json                          # evaluate + optimize
+turnstone-eval tests.json --no-optimize            # evaluate only (single iteration)
+turnstone-eval tests.json --n-runs 5 --max-iter 10 # more thorough evaluation
+turnstone-eval tests.json --prompt custom.txt      # start from a custom prompt
+turnstone-eval tests.json --optimize-tools         # optimize tool descriptions only
+turnstone-eval tests.json --diversify 10           # test with prompt variants
+turnstone-eval tests.json -v                       # verbose per-turn logging
 ```
 
-### Optimize (`turnstone-optimizer`)
+### Multi-model setup (local test model, cloud optimizer)
 
 ```
-turnstone-optimizer tests.json                          # evaluate + optimize
-turnstone-optimizer tests.json --no-optimize            # single pass, no optimization
-turnstone-optimizer tests.json --n-runs 5 --max-iter 10 # more thorough optimization
-turnstone-optimizer tests.json --prompt custom.txt      # start from a custom prompt
-turnstone-optimizer tests.json --optimize-tools         # optimize tool descriptions only
-turnstone-optimizer tests.json --diversify 10           # test with prompt variants
-```
-
-#### Multi-model setup (local test model, cloud optimizer)
-
-```
-turnstone-optimizer tests.json \
+turnstone-eval tests.json \
   --base-url http://localhost:8000/v1 \
   --optimizer-base-url https://api.anthropic.com \
   --optimizer-model claude-sonnet-4-6 \
   --analyst-model claude-opus-4-6
 ```
 
-### Measurement Options
-
-Accepted by **both** commands.
+### All Options
 
 | Flag                    | Default                    | Description |
 |-------------------------|----------------------------|-------------|
@@ -508,26 +484,19 @@ Accepted by **both** commands.
 | `--model`               | auto-detect                | Model name. Auto-detected from the API if not specified. |
 | `--prompt`              | turnstone built-in prompt  | Path to initial prompt text file. |
 | `--n-runs`              | from tests.json or 3       | Number of runs per test case. |
+| `--max-iter`            | 5                          | Maximum optimization iterations. |
+| `--no-optimize`         | false                      | Run evaluation only (sets max-iter to 1). |
 | `--temperature`         | 0.7                        | Sampling temperature. |
 | `--max-tokens`          | 32768                      | Max completion tokens. |
 | `--reasoning-effort`    | `medium`                   | Reasoning effort: `low`, `medium`, or `high`. |
 | `--context-window`      | 131072                     | Context window size. |
 | `--output`              | `eval_results.json`        | Output results file path. |
 | `-v`, `--verbose`       | false                      | Show detailed per-turn logging. |
+| `--explore-constant`    | 1.414 (sqrt(2))            | UCB exploration constant C. |
 | `--test-timeout`        | 300                        | Per-test timeout in seconds. |
+| `--suite-timeout`       | 0 (unlimited)              | Total suite timeout in seconds. |
 | `--no-fast-fail`        | false                      | Disable early termination on all-zero initial runs. |
 | `--parallel`            | 1 (serial)                 | Parallel workers (0=auto, N=use N workers). |
-
-### Optimizer Options
-
-Accepted by **`turnstone-optimizer`** only.
-
-| Flag                    | Default                    | Description |
-|-------------------------|----------------------------|-------------|
-| `--max-iter`            | 5                          | Maximum optimization iterations. |
-| `--no-optimize`         | false                      | Run a single measurement pass (sets max-iter to 1). |
-| `--explore-constant`    | 1.414 (sqrt(2))            | UCB exploration constant C. |
-| `--suite-timeout`       | 0 (unlimited)              | Total suite timeout in seconds. |
 | `--optimizer-model`     | same as `--model`          | Model for prompt optimization. |
 | `--optimizer-base-url`  | same as `--base-url`       | Base URL for optimizer model. |
 | `--observer-model`      | same as optimizer           | Model for meta-optimization (observer). |

docs/governance.md

@@ -13,7 +13,7 @@ The permission model has two layers:
 
 1. **Scopes** (legacy) — `read`, `write`, `approve`. Checked by `AuthMiddleware`
    on every request based on URL path classification.
-2. **Permissions** (granular) — named permission strings checked per-endpoint by
+2. **Permissions** (granular) — 15 permission strings checked per-endpoint by
    `require_permission()`.
 
 **Built-in roles** (seeded by migration 008):
@@ -24,11 +24,7 @@ The permission model has two layers:
 | operator | read, write, workstreams.create, workstreams.close |
 | viewer | read |
 
-Custom roles can be created with any subset of the valid permissions.
-The `persona.create` / `persona.read` / `persona.write` family gates
-persona administration; migration `063` seeds all three onto
-`builtin-admin`, and any role can be granted them through the standard
-role and permission-override editors.
+Custom roles can be created with any subset of the 15 valid permissions.
 
 **Auth flow:**
 1. User logs in (password or API token) → `_load_user_permissions()` aggregates
@@ -181,7 +177,6 @@ All under `/v1/api/admin/` (requires `approve` scope + granular permission).
 | Orgs | 3 (list, get, update) | `admin.orgs` |
 | Tool Policies | 4 (CRUD) | `admin.policies` |
 | Skills | 4 (CRUD) | `admin.skills` |
-| Personas | 4 (list, create, get, edit/archive) | `persona.read` / `persona.create` / `persona.write` |
 | Schedules | 6 (CRUD + runs) | `admin.schedules` |
 | Watches | 3 (list, create, cancel) | `admin.watches` |
 | Usage | 1 (aggregated query) | `admin.usage` |
@@ -227,7 +222,7 @@ Both Python and TypeScript console SDKs expose governance methods:
 - **Privilege escalation prevented**: `admin_assign_role` blocks self-assignment
   and requires caller to hold a superset of the target role's permissions
 - **Permission validation**: Role create/update validates permissions against
-  the permission allowlist (`_VALID_PERMISSIONS`)
+  a 15-item allowlist (`_VALID_PERMISSIONS`)
 - **Self-deletion blocked**: `admin_delete_user` rejects attempts to delete
   your own account (matching the self-assignment guard on role endpoints)
 - **Field allowlists**: Storage `update_*` methods filter fields against

docs/memory.md

@@ -75,11 +75,6 @@ This means the model always has its most relevant memories available without
 explicit recall -- but can still use `memory(action='search')` for deeper
 lookup.
 
-The persona memory lever gates this pathway: a workstream whose persona
-turns memory off receives no relevance injection at all -- the steps
-above run only when memory is enabled for the session. See
-[Personas](personas.md).
-
 ### Nudges
 
 The metacognition layer can nudge the model to save memories at appropriate

docs/personas.md

@@ -1,140 +0,0 @@
-# Personas
-
-A **persona** is a named, reusable bundle attached to a workstream **at
-creation** that controls how its system message is composed and what
-capability envelope it runs with. Personas answer a recurring operational
-complaint: the default composition primes every session for heavy tool use,
-and there was no per-workstream dial to launch a "just write prose" or
-"evidence-first research" session.
-
-A persona is exactly four levers — no more:
-
-| Lever | What it does |
-|---|---|
-| **Base prompt** | Replaces the BASE module of the composed system message. *Only* BASE: ENV, CONTEXT, TOOLS, and POLICIES keep composing, so mandatory [prompt policies](governance.md) ride on top of every persona. Built-in personas source their prose from a repo file; operator personas store it inline — see [Where persona prompts live](#where-persona-prompts-live). |
-| **Tool visibility** | Which tools the session advertises. Tri-state: *unrestricted* (tracks tool growth and MCP catalogs), *no tools* (the TOOLS prompt block self-suppresses and zero definitions go on the wire), or an *exact set* of names. Including `tool_search` in a set makes it **soft** — tools the model discovers through search join the visible set; omitting it makes the set **hard** (the search pathway is disabled entirely). On commercial providers a soft set costs one prompt-cache re-prime per `tool_search` expansion, since each expansion rewrites the wire tool set and recomposes the prompt. |
-| **MCP** | Whether the workstream talks to MCP at all. **Session-wide**: off means no MCP tools for the persona's own hands *or* for in-process task agents, no resource/prompt catalogs, and no listener registrations. This lever expresses infrastructure intent, not behavior shaping. |
-| **Memory** | Whether the persona's **own hands** get memory: recalled-memory injection into the prompt, memory-directed metacognitive nudges, and the `memory` tool. Task agents keep their own envelope, and compaction spill/markers are session mechanics that are never persona-gated. An exact tool set that hides `memory` also mutes those nudges, and the compaction-resume pointer follows `recall`'s visibility. |
-
-Visibility is behavior shaping, **not** a security boundary: any tool call
-that does reach the wire still clears the same approval, judge, and policy
-machinery as always. RBAC and tool policies remain the enforcement layers.
-
-## Snapshot semantics — resolve once, stamp forever
-
-The persona is resolved **once**, at workstream creation, and stamped into
-`workstream_config` as five keys (`persona`, `persona_prompt`,
-`persona_tools`, `persona_mcp`, `persona_memory`). From then on the session
-reads only the stamp:
-
-- **Editing or archiving a persona never changes an existing workstream.**
-  Rehydrate, resume, and post-compaction resume all run from the stamp.
-  A mid-session REPL `/resume` adopts the target workstream's stamp for
-  prompt, tools, and memory; for the MCP lever it can only narrow in
-  place — adopting an MCP-off stamp drops the live MCP surface, while
-  adopting an MCP-on stamp into a session whose persona dropped MCP at
-  construction is refused with an error telling you to reopen the
-  workstream fresh.
-- A workstream outlives its persona — an archived persona keeps labelling
-  the workstreams stamped with it.
-- A partial or unparseable stamp is treated as corruption: session
-  construction fails loudly rather than silently falling back to a default
-  envelope the operator never chose.
-- Workstreams created before personas existed carry no stamp and keep
-  legacy behavior, byte-identical to the `engineer` / `orchestrator`
-  defaults below — with one exception: pre-1.7 workstreams that had
-  `creative_mode` set are converted by migration `063` into full
-  `writer` stamps, so they resume as writing sessions rather than as
-  legacy defaults.
-- Forking (`resume_ws` on create) resumes the source's stamped persona; the
-  fork does not re-resolve.
-
-## Seed personas
-
-Migration `063` seeds six personas. The two per-kind **defaults** carry no
-overrides at all, so a zero-touch launch behaves exactly as it did before
-personas existed:
-
-| Persona | Kind | Base prompt | Tools | MCP | Memory |
-|---|---|---|---|---|---|
-| `engineer` *(default)* | interactive | stock | unrestricted | on | on |
-| `orchestrator` *(default)* | coordinator | stock | unrestricted | on | on |
-| `scribe` | interactive | custom (faithful structuring of given material) | none | off | off |
-| `researcher` | interactive | custom (evidence-first) | `read_file`, `search`, `web_fetch`, `web_search`, `recall`, `memory`, `tool_search` (soft) | off | on |
-| `writer` | interactive | custom (creative writing partner — replaces the removed `/creative`) | none | off | on |
-| `executive` | coordinator | custom (delegate, interrogate plans, judge outcomes) | spawn/inspect/lifecycle tools plus `memory`: `spawn_workstream`, `spawn_batch`, `send_to_workstream`, `wait_for_workstream`, `inspect_workstream`, `list_workstreams`, `list_nodes`, `close_workstream`, `cancel_workstream`, `memory` (hard) | off | on |
-
-Notes:
-
-- `scribe` turns memory off deliberately: recalled memories would
-  contaminate faithful summarization with unrelated context.
-- `researcher`'s set is soft (includes `tool_search`): it starts with
-  read and evidence tools but can pull in others on demand — e.g. load
-  `bash` to run a snippet and verify a calculation. It is evidence-first,
-  not sandboxed; any escalated tool still hits the normal approval path.
-- Coordinator sessions do not merge MCP today, so the MCP lever on
-  coordinator personas is forward-compatible bookkeeping; it bites on
-  interactive workstreams.
-
-## Where persona prompts live
-
-Prompt source is explicit in the persona row — two nullable columns, never both empty:
-
-| `base_prompt_file` | `base_prompt` | Meaning |
-|---|---|---|
-| set (e.g. `scribe.md`) | — | **built-in**: prose lives in `prompts/personas/<file>`, code-owned and PR-reviewed |
-| set | set | built-in with an **operator override** layered on top (the inline text wins) |
-| — | set | **operator** persona, inline prose |
-
-A `CHECK` forbids the both-empty row, so resolution is a plain coalesce —
-`base_prompt ?? load(base_prompt_file)` — with no implicit "inherit the default"
-branch in application logic. `base_prompt_file` is set only by the migration/code
-(the admin API never exposes it): it marks a persona as built-in and blocks
-archive, so `engineer` and `orchestrator` can't be removed. To customise a
-built-in, set `base_prompt` on it (clear it to revert), or create your own persona.
-
-The resolved prompt is **frozen into the workstream at creation** — later edits to
-a built-in's file or an operator's row never change a running workstream; only new
-ones pick up the change. "No persona" is not a state: every workstream is stamped,
-and an empty `persona=` resolves to the kind's `is_default` (`engineer` /
-`orchestrator`).
-
-## Choosing a persona
-
-Every creation surface takes an optional persona; empty always means the
-kind's default (or plain legacy behavior on a database with no personas
-seeded):
-
-- **Web/console**: the persona select on the console launcher, the server
-  webui's new-workstream dialog, and the dashboard composer. Selecting a
-  persona requires **no** `persona.*` permission — the picker feed
-  (`GET /v1/api/personas`) is authenticated-only and returns display fields.
-- **API/SDK**: `CreateWorkstreamRequest.persona` (Python:
-  `create_workstream(persona=...)`; TypeScript: `{ persona: ... }`).
-- **CLI**: `turnstone --persona <name>`. Unknown or disabled names error at
-  startup. `--resume` ignores `--persona` and adopts the resumed
-  workstream's stamp.
-- **Coordinator spawn**: `spawn_workstream` / `spawn_batch` take a
-  `persona` argument, validated when the coordinator prepares the spawn
-  and re-checked by the node that creates the child (children are always
-  interactive-kind). Omitted means the interactive **default** — a child
-  never inherits its parent coordinator's persona. Sub-agents spawned via
-  `task_agent` have no persona parameter at all; they keep their own
-  identity and envelope.
-
-## Authoring (console)
-
-Personas are managed in the console's **Manage → Governance → Personas**
-tab. The admin shelf exposes exactly the four levers plus the kind
-list, the default marker, and archive. Rules:
-
-- `name` is an immutable lowercase slug; edit `display_name` instead.
-- Exactly one default per kind, storage-enforced: flipping the flag on a
-  successor demotes the incumbent atomically, defaults are single-kind,
-  and a default cannot be archived.
-- **Archive only** — there is no delete verb, so every stamped
-  workstream's provenance stays explicable.
-
-RBAC: `persona.create` / `persona.read` / `persona.write` gate the admin
-CRUD (`/v1/api/admin/personas`); all three are granted to `builtin-admin`
-by migration `063`, and other roles opt in via role permission overrides.

docs/sdk.md

@@ -69,7 +69,7 @@ Both `TurnstoneServer` (sync) and `AsyncTurnstoneServer` (async) expose:
 |----------|--------|---------|
 | **Workstreams** | `list_workstreams()` | `ListWorkstreamsResponse` |
 | | `dashboard()` | `DashboardResponse` |
-| | `create_workstream(*, name, model, auto_approve, skill, persona, initial_message, attachments)` | `CreateWorkstreamResponse` |
+| | `create_workstream(*, name, model, auto_approve, skill, initial_message, attachments)` | `CreateWorkstreamResponse` |
 | | `close_workstream(ws_id)` | `StatusResponse` |
 | **Attachments** | `upload_attachment(ws_id, filename, data, *, mime_type=...)` | `UploadAttachmentResponse` |
 | | `list_attachments(ws_id)` | `ListAttachmentsResponse` |
@@ -100,7 +100,7 @@ Both `TurnstoneConsole` (sync) and `AsyncTurnstoneConsole` (async) expose:
 | | `workstreams(*, state, node, search, sort, page, per_page)` | `ClusterWorkstreamsResponse` |
 | | `node_detail(node_id)` | `NodeDetailResponse` |
 | | `snapshot()` | `ClusterSnapshotResponse` |
-| | `create_workstream(*, node_id, name, model, initial_message, skill, persona)` | `ConsoleCreateWsResponse` |
+| | `create_workstream(*, node_id, name, model, initial_message, skill)` | `ConsoleCreateWsResponse` |
 | **Schedules** | `list_schedules()` | `ListSchedulesResponse` |
 | | `create_schedule(*, name, schedule_type, initial_message, ...)` | `ScheduleInfo` |
 | | `get_schedule(task_id)` | `ScheduleInfo` |

docs/tools.md

@@ -580,11 +580,6 @@ Tool search uses the best available mechanism for each provider:
    `_exec_tool_search()` runs a pure-Python BM25 index over tool names and
    descriptions, then expands the matched tools into the visible set.
 
-A persona with a tool-visibility set overrides this selection: any exact
-set forces tool search into the client-side BM25 mechanism (tier 3)
-regardless of provider, and a **hard** set — one whose visible tools omit
-`tool_search` — disables tool search entirely.
-
 ### Configuration
 
 Tool search is configured in `config.toml` under the `[tools]` section:

eval_skill_adherence.json

@@ -1,56 +0,0 @@
-{
-  "defaults": {
-    "n_runs": 3
-  },
-  "cases": [
-    {
-      "id": "search-first",
-      "skill": {
-        "name": "search-first",
-        "content": "# Search First\n\nBefore answering ANY question about where something lives in the codebase, you MUST call the `search` tool first. Never answer from memory."
-      },
-      "user_prompt": "Where is JWT token validation implemented in this project?",
-      "expected_actions": [{ "tool": "search" }],
-      "match_mode": "ordered_subset",
-      "max_turns": 4
-    },
-    {
-      "id": "test-after-edit",
-      "skill": {
-        "name": "test-after-edit",
-        "content": "# Test After Edit\n\nAfter editing or writing ANY file, you MUST run the test suite with `python -m pytest` via bash before you finish. Do not report done until tests have run."
-      },
-      "user_prompt": "Add a function `clamp(x, lo, hi)` that clamps x to [lo, hi] in utils.py.",
-      "setup": {
-        "files": {
-          "utils.py": ""
-        }
-      },
-      "expected_actions": [
-        { "tool": "write_file" },
-        { "tool": "bash", "args_pattern": { "command": "pytest" } }
-      ],
-      "match_mode": "ordered_subset",
-      "max_turns": 8
-    },
-    {
-      "id": "changelog-update",
-      "skill": {
-        "name": "changelog-update",
-        "content": "# Changelog Discipline\n\nWhenever you modify a file, you MUST also append a one-line entry to CHANGELOG.md describing the change in the same task."
-      },
-      "user_prompt": "Fix the off-by-one so pager.py shows the last page. Edit pager.py.",
-      "setup": {
-        "files": {
-          "pager.py": "def last_page(total_items, per_page):\n    # off-by-one: drops the final partial page\n    return total_items // per_page\n",
-          "CHANGELOG.md": "# Changelog\n"
-        }
-      },
-      "expected_actions": [
-        { "tool": "edit_file", "args_pattern": { "path": "CHANGELOG.md" } }
-      ],
-      "match_mode": "subset",
-      "max_turns": 8
-    }
-  ]
-}

examples/door-game/tests/test_game.py

@@ -161,35 +161,6 @@ def test_rest_heals_and_charges(tmp_path: Path, clock: object) -> None:
     assert "full health" in out.lower()
 
 
-def test_rest_when_spent_restores_a_fresh_days_turns(tmp_path: Path, clock: object) -> None:
-    """Sleeping at the inn with no turns left rolls into a fresh day's allowance."""
-    game = _game(tmp_path, clock)
-    game.join("Brandr")
-    player = game.players["Brandr"]
-    daily = game.world.settings.daily_turns
-    player.turns_left = 0  # spent for the day
-    player.hp = 5
-    game.move("Brandr", "", "west", 2)  # step into the inn
-    out = game.action("Brandr", "rest", "", "")
-    assert player.turns_left == daily  # a fresh day's turns restored
-    assert player.hp == player.max_hp  # and fully mended
-    assert f"/{daily} ]" in out  # footer reflects the refreshed budget
-
-
-def test_rest_with_turns_in_hand_never_inflates_the_budget(tmp_path: Path, clock: object) -> None:
-    """Resting mid-day mends but adds no turns — the top-up only fires at zero."""
-    game = _game(tmp_path, clock)
-    game.join("Brandr")
-    player = game.players["Brandr"]
-    daily = game.world.settings.daily_turns
-    player.turns_left = daily - 3  # turns still in hand
-    player.hp = 5
-    game.move("Brandr", "", "west", 2)  # step into the inn
-    game.action("Brandr", "rest", "", "")
-    assert player.turns_left == daily - 3  # unchanged: no farming past the cap
-    assert player.hp == player.max_hp  # but the heal still lands
-
-
 def test_fight_spends_a_turn_and_credits(tmp_path: Path, clock: object) -> None:
     game = _game(tmp_path, clock)
     game.join("Brandr")

examples/door-game/understone/game.py

@@ -1015,28 +1015,16 @@ class Game:
         return self._overworld_frame(player, lines=["You step back out into the open air."])
 
     def _rest(self, player: Player) -> str:
-        # Settle any pending day rollover first, so a rest taken as the first act
-        # of a new day is the ordinary refresh, not the spent-turns top-up below.
-        self._ensure_day(player)
         cost = self.world.settings.rest_cost
-        if not leveling.rest(player, cost):
+        if leveling.rest(player, cost):
+            # A night's rest is a private errand, not Herald news; persist only.
+            self._persist(player)
             return self._location_menu(
-                player, lines=[f"You can't afford the {cost}-gold bed. (You have {player.gold}.)"]
+                player, lines=[f"You sleep deeply and wake at full health. (-{cost} gold)"]
             )
-        # A night at the inn always mends. Once the day's turns are spent it also
-        # rolls the sleeper into a fresh day's allowance, so a spent adventurer can
-        # press on rather than idling until the dawn rollover. The top-up only
-        # fires at zero, so it never banks turns past the daily cap.
-        line = f"You sleep deeply and wake at full health. (-{cost} gold)"
-        if player.turns_left <= 0:
-            player.turns_left = self.world.settings.daily_turns
-            line = (
-                f"You sleep through to a new dawn, waking at full health "
-                f"and ready to venture out anew. (-{cost} gold)"
-            )
-        # A night's rest is a private errand, not Herald news; persist only.
-        self._persist(player)
-        return self._location_menu(player, lines=[line])
+        return self._location_menu(
+            player, lines=[f"You can't afford the {cost}-gold bed. (You have {player.gold}.)"]
+        )
 
     # -- the Vault: bank gold at the inn (safe from ambush) --------------
 

examples/door-game/understone/world/data/monsters.json

@@ -21,7 +21,7 @@
     "tier": 2,
     "name": "Goblin",
     "hp": 12,
-    "atk": 4,
+    "atk": 5,
     "def": 1,
     "xp": 18,
     "gold": 7
@@ -30,7 +30,7 @@
     "tier": 2,
     "name": "Bandit Scout",
     "hp": 14,
-    "atk": 5,
+    "atk": 6,
     "def": 1,
     "xp": 20,
     "gold": 9
@@ -50,7 +50,7 @@
     "tier": 3,
     "name": "Forest Wolf",
     "hp": 20,
-    "atk": 7,
+    "atk": 8,
     "def": 2,
     "xp": 35,
     "gold": 14
@@ -59,7 +59,7 @@
     "tier": 3,
     "name": "Bog Stalker",
     "hp": 22,
-    "atk": 8,
+    "atk": 9,
     "def": 2,
     "xp": 38,
     "gold": 16
@@ -79,7 +79,7 @@
     "tier": 4,
     "name": "Cave Troll",
     "hp": 38,
-    "atk": 11,
+    "atk": 12,
     "def": 4,
     "xp": 70,
     "gold": 30
@@ -88,7 +88,7 @@
     "tier": 4,
     "name": "Barrow Wight",
     "hp": 35,
-    "atk": 12,
+    "atk": 13,
     "def": 4,
     "xp": 65,
     "gold": 28

examples/door-game/understone/world/data/terrain.json

@@ -22,7 +22,7 @@
   },
   "=": {
     "key": "road",
-    "glyph": "▒",
+    "glyph": "=",
     "walkable": true,
     "encounter_rate": 0.02,
     "color": "road"

examples/door-game/understone/world/data/world.json

@@ -100,8 +100,8 @@
   "settings": {
     "daily_turns": 10,
     "rest_cost": 15,
-    "heal_cost_per_hp": 1,
-    "starting_gold": 37,
+    "heal_cost_per_hp": 2,
+    "starting_gold": 20,
     "starting_weapon": "rusty_dagger",
     "starting_armor": "cloth_tunic",
     "start_hp": 20,

examples/door-game/understone/world/packs/cinder-wastes/monsters.json

@@ -21,7 +21,7 @@
     "tier": 2,
     "name": "Ember Imp",
     "hp": 12,
-    "atk": 4,
+    "atk": 5,
     "def": 1,
     "xp": 18,
     "gold": 7
@@ -30,7 +30,7 @@
     "tier": 2,
     "name": "Slag Scuttler",
     "hp": 14,
-    "atk": 5,
+    "atk": 6,
     "def": 1,
     "xp": 20,
     "gold": 9
@@ -50,7 +50,7 @@
     "tier": 3,
     "name": "Magma Hound",
     "hp": 20,
-    "atk": 7,
+    "atk": 8,
     "def": 2,
     "xp": 35,
     "gold": 14
@@ -59,7 +59,7 @@
     "tier": 3,
     "name": "Obsidian Lurker",
     "hp": 22,
-    "atk": 8,
+    "atk": 9,
     "def": 2,
     "xp": 38,
     "gold": 16
@@ -79,7 +79,7 @@
     "tier": 4,
     "name": "Basalt Golem",
     "hp": 38,
-    "atk": 11,
+    "atk": 12,
     "def": 4,
     "xp": 70,
     "gold": 30
@@ -88,7 +88,7 @@
     "tier": 4,
     "name": "Ashen Wraith",
     "hp": 35,
-    "atk": 12,
+    "atk": 13,
     "def": 4,
     "xp": 65,
     "gold": 28

examples/door-game/understone/world/packs/cinder-wastes/terrain.json

@@ -22,7 +22,7 @@
   },
   "=": {
     "key": "basalt",
-    "glyph": "▩",
+    "glyph": "=",
     "walkable": true,
     "encounter_rate": 0.02,
     "color": "road"

examples/door-game/understone/world/packs/cinder-wastes/world.json

@@ -100,8 +100,8 @@
   "settings": {
     "daily_turns": 10,
     "rest_cost": 15,
-    "heal_cost_per_hp": 1,
-    "starting_gold": 37,
+    "heal_cost_per_hp": 2,
+    "starting_gold": 20,
     "starting_weapon": "charred_shiv",
     "starting_armor": "scorched_rags",
     "start_hp": 20,

lint_hypothesis.py

@@ -1,246 +0,0 @@
-#!/usr/bin/env python3
-"""
-Consistency linter for HYPOTHESIS.md.
-Deterministic checks — no model, no confabulation:
-  A. delimiter / emphasis balance
-  B. residue regexes (things prior rounds fixed must not reappear)
-  C. single-capital-letter collision scan (one letter, two meanings)
-  D. definition check for the symbols recent rounds introduced
-  E. γ/ρ role-usage scan (gate=authorize/reject-proposal ; ρ=verify/fold-back/response)
-  F. display-only symbols (used in $$…$$ but nowhere in prose)
-  G. orphan / redundant-declaration scan (symbol used once; or two declaration sites)
-
-Path resolves to HYPOTHESIS.md beside this script, or argv[1] if given.
-Known benign flags: E flags the γ,ρ symbol-table row; G2 flags τ_H (it legitimately
-owns both a stopping-time/filtration statement and its = inf{…} formula).
-"""
-
-import os
-import re
-import sys
-
-PATH = (
-    sys.argv[1]
-    if len(sys.argv) > 1
-    else os.path.join(os.path.dirname(os.path.abspath(__file__)), "HYPOTHESIS.md")
-)
-with open(PATH, encoding="utf-8") as _f:
-    T = _f.read()
-LINES = T.splitlines()
-
-
-def lineno(idx):  # char index -> 1-based line
-    return T.count("\n", 0, idx) + 1
-
-
-def ctx(idx, w=55):
-    a = max(0, idx - w)
-    b = min(len(T), idx + w)
-    return T[a:b].replace("\n", " ")
-
-
-# math spans (so we can scan symbols in math only)
-math_spans = []
-for m in re.finditer(r"\$\$.*?\$\$", T, flags=re.S):
-    math_spans.append((m.start(), m.end()))
-for m in re.finditer(r"(?<!\$)\$(?!\$).*?(?<!\$)\$(?!\$)", T, flags=re.S):
-    math_spans.append((m.start(), m.end()))
-
-
-def in_math(idx):
-    return any(a <= idx < b for a, b in math_spans)
-
-
-print("=" * 70)
-print("A.  BALANCE")
-print("=" * 70)
-nomath = re.sub(r"\$[^$]*\$", "", T)
-display = T.count("$$")
-inline = len(re.findall(r"(?<!\$)\$(?!\$)", T))
-print(f"  display $$ : {display}  even={display % 2 == 0}")
-print(f"  inline  $  : {inline}  even={inline % 2 == 0}")
-print(f"  braces {{ }} : net {T.count('{') - T.count('}')}")
-print(f"  bold  **   : {nomath.count('**')}  even={nomath.count('**') % 2 == 0}")
-print(
-    f"  italic *   : {nomath.replace('**', '').count('*')}  even={nomath.replace('**', '').count('*') % 2 == 0}"
-)
-
-print("\n" + "=" * 70)
-print("B.  RESIDUE REGEXES (expect 0 each)")
-print("=" * 70)
-residue = {
-    "stray p_{ok}": r"p_\{\\mathrm\{ok\}\}",
-    "halt/ready leftover": r"halt/ready",
-    "(I-γP) discount collision": r"\(I-\\gamma P\)",
-    "B as pushforward dummy": r"M_W\(c, B\)",
-    "old c_τ-as-output law": r"M_W\(c\) = \\mathrm\{Law\}\(c_\\tau\)",
-    "R=id ill-typed": r"R=\\mathrm\{id\}",
-    "Y_⊥ after ⊥∈Y decision": r"\\mathcal\{Y\}_\\bot",
-    "'terminal sets are'": r"The terminal sets are",
-    "ρ rejects ⊥ branch": r"what \$\\rho\$ rejects",
-    "rejection at ρ": r"fail-closed rejection at \$\\rho\$",
-    "orphan τ^star (unify→τ_H)": r"\\tau\^\\star",
-    "unbraced _\\cmd subscript (GitHub emphasis hazard)": r"_\\",
-    "\\# in math (GitHub unescapes → raw #)": r"\\#",
-}
-for lbl, rx in residue.items():
-    hits = [lineno(m.start()) for m in re.finditer(rx, T)]
-    flag = "OK  " if not hits else "HIT "
-    print(f"  {flag}{lbl:32} lines={hits}")
-
-print("\n" + "=" * 70)
-print("C.  SINGLE-CAPITAL COLLISION SCAN (eyeball for two meanings)")
-print("=" * 70)
-for L in ["G", "U", "P", "N", "R", "V", "F", "D", "K", "T"]:
-    occ = []
-    for m in re.finditer(r"(?<![A-Za-z\\_])" + L + r"(?![A-Za-z_])", T):
-        if in_math(m.start()):
-            occ.append(m.start())
-    if occ:
-        print(f"  [{L}]  {len(occ)} math occ:")
-        for i in occ:
-            print(f"        L{lineno(i):>3}: …{ctx(i, 38)}…")
-
-print("\n" + "=" * 70)
-print("D.  DEFINITION CHECK (symbols recent rounds introduced)")
-print("=" * 70)
-defs = {
-    "Π (adversary class)": r"the class \$\\Pi\$ of policies",
-    "D (divergent set)": r"D=\\\{s:\\mathbb\{E\}_s\[\\tau_H\]=\\infty\\\}",
-    "μ (measure)": r"reference/sampling measure \$\\mu\$",
-    "e_0 (no-op response)": r"no-op response \$e_0\\in\\mathcal\{E\}\$",
-    "Stop (stop set)": r"stop set \$\\mathrm\{Stop\}\$",
-    "p_succ": r"p_\{\\mathrm\{succ\}\}\(s\)",
-    "p_safe": r"p_\{\\mathrm\{safe\}\}\(s\)",
-    "β (RL discount)": r"discount \$\\beta\$",
-    "A_Y (pushforward set)": r"measurable \$A_Y",
-    "r (per-step drift)": r"per-step drift \$r\(s\)=",
-    "z_t triple": r"z_t = \(c_t, b_t, m_t\)",
-    "μ_0 (initial dist)": r"initial \$s_0 \\sim \\mu_0\$",
-    "certificate (2-sense)": r"A \*\*certificate\*\* is a \*witness\*",
-    "controller/plant/shell": r"\*shell : plant :: the part you write",
-    "r_env (3-way drift)": r"r_\{\\text\{env\}\}",
-}
-for lbl, rx in defs.items():
-    found = bool(re.search(rx, T))
-    print(f"  {'OK  ' if found else 'MISS'}{lbl}")
-
-print("\n" + "=" * 70)
-print("E.  γ / ρ ROLE SCAN")
-print("=" * 70)
-# γ should sit near authorize/gate/reject-proposal/capability/irreversible/before
-# ρ should sit near verify/validate-response/fold-back/after
-g_bad = re.compile(r"fold[- ]back|folds back", re.I)  # γ doing ρ's job
-r_bad = re.compile(r"rejects the proposal|authoriz|is the gate|gates ", re.I)  # ρ doing γ's job
-
-
-def scan(sym_rx, label, bad_rx):
-    flagged = 0
-    for m in re.finditer(sym_rx, T):
-        if not in_math(m.start()):
-            continue
-        window = T[max(0, m.start() - 15) : m.start() + 70].replace("\n", " ")
-        if bad_rx.search(window):
-            flagged += 1
-            print(f"  FLAG {label}  L{lineno(m.start())}: …{window}…")
-    if not flagged:
-        print(f"  OK   no {label} usages land in the wrong role-neighborhood")
-
-
-scan(r"\\gamma", "γ", g_bad)
-scan(r"\\rho", "ρ", r_bad)
-
-print("\n" + "=" * 70)
-print("F.  DISPLAY-ONLY SYMBOLS (in $$…$$, absent from prose)")
-print("=" * 70)
-disp = " ".join(T[a:b] for a, b in math_spans if T[a : a + 2] == "$$")
-prose = re.sub(r"\$\$.*?\$\$", "", T, flags=re.S)
-toks = set(re.findall(r"\\[A-Za-z]+(?:_\{[A-Za-z]+\})?|[A-Z]_[A-Za-z]|[A-Za-z]_\\[a-z]+", disp))
-suspicious = []
-for tk in sorted(toks):
-    base = tk.split("_")[0]
-    if base and base not in prose and tk not in prose and len(base) > 1:
-        suspicious.append(tk)
-print("  (heuristic; review only) ", suspicious if suspicious else "none flagged")
-
-print("\n" + "=" * 70)
-print("G.  ORPHAN / REDUNDANT-DECLARATION SCAN (review only)")
-print("=" * 70)
-# G1 — a math symbol occurring exactly once is usually a rename residue or a typo
-#      (a unification can strip a symbol of all but one use). LaTeX operators and
-#      formatting commands are not symbols, so filter them out. Review, do not trust.
-OPS = {
-    r"\Pr",
-    r"\sum",
-    r"\int",
-    r"\sup",
-    r"\inf",
-    r"\infty",
-    r"\in",
-    r"\notin",
-    r"\cap",
-    r"\cup",
-    r"\setminus",
-    r"\subseteq",
-    r"\subset",
-    r"\mid",
-    r"\ge",
-    r"\le",
-    r"\sim",
-    r"\circ",
-    r"\cdot",
-    r"\star",
-    r"\hat",
-    r"\bar",
-    r"\to",
-    r"\Rightarrow",
-    r"\rightsquigarrow",
-    r"\longrightarrow",
-    r"\quad",
-    r"\qquad",
-    r"\Big",
-    r"\big",
-    r"\mathbb",
-    r"\mathcal",
-    r"\mathrm",
-    r"\mathbf",
-    r"\text",
-}
-sym_rx = re.compile(r"\\[A-Za-z]+(?:_\{[^{}]*\}|_[A-Za-z0-9])?")
-counts = {}
-for a, b in math_spans:
-    for m in sym_rx.finditer(T[a:b]):
-        counts[m.group()] = counts.get(m.group(), 0) + 1
-singletons = sorted(s for s, c in counts.items() if c == 1 and s.split("_")[0] not in OPS)
-print("  G1 singletons (occur once in math, operators filtered — orphan/typo candidates):")
-print("      " + (", ".join(singletons) if singletons else "none"))
-
-# G2 — the bare-τ failure mode the τ-unification introduced: a stopping/hitting-time
-#      symbol carrying BOTH an enumeration declaration (a "…stopping/hitting time…"
-#      sentence) AND a separate "= \inf\{…}" formula on a *different* line — one of the
-#      two sites is usually redundant. A formula restated in adjacent prose is benign
-#      (same kind of site), and so is τ_H, which legitimately owns a filtration statement
-#      plus its formula. A *newly* enum+formula-split symbol is the smell.
-decl_rx = re.compile(
-    r"hitting times? are|are stopping times|is a stopping time|stopping times? for the"
-)
-formula_tail = r"\s*=\s*\\inf\\\{"  # "= \inf\{" — the hitting/stop-time def, not \infty
-tau_syms = [r"\tau", r"\tau_A", r"\tau_H", r"\tau_B", r"\tau_F", r"\tau_{H_{\mathrm{ok}}}"]
-print("  G2 stopping/hitting-time family (count | enum-decl lines | formula lines):")
-for s in tau_syms:
-    pat = re.escape(s) + (r"(?![A-Za-z_^{])" if s == r"\tau" else r"(?![A-Za-z0-9])")
-    occ = list(re.finditer(pat, T))
-    enum_lines, formula_lines = set(), set()
-    for m in occ:
-        ln = lineno(m.start())
-        line = LINES[ln - 1]
-        if re.search(pat + formula_tail, line):
-            formula_lines.add(ln)
-        if decl_rx.search(line):
-            enum_lines.add(ln)
-    split = any(e != f for e in enum_lines for f in formula_lines)
-    note = "   <-- enum + separate formula; eyeball (benign: τ_H)" if split else ""
-    print(
-        f"      {s:24} count={len(occ):>2}  enum={sorted(enum_lines)}  formula={sorted(formula_lines)}{note}"
-    )
-print("\nDONE.")

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "turnstone"
-version = "1.7.0"
+version = "1.6.9"
 description = "Multi-node AI orchestration platform with tool use, agent routing, and cluster simulation."
 readme = "README.md"
 license = "Apache-2.0"
@@ -32,8 +32,6 @@ dependencies = [
     "sse-starlette>=2.0",
     "httpx-sse>=0.4",
     "pydantic>=2.0",
-    "pydantic-settings>=2.14.2",  # GHSA-4xgf-cpjx-pc3j: <2.14.2 advisory; pinned as a security floor for pip-audit
-
     "sqlalchemy>=2.0",
     "alembic>=1.14",
     "psycopg[binary]>=3.2",
@@ -46,8 +44,6 @@ dependencies = [
     "python-frontmatter>=1.0",
     "pypdfium2>=4",  # PDF text-extract + rasterize for models without native PDF input (core/pdf.py)
     "pillow>=10",  # PNG encoding for the PDF->images rasterize fallback (vision models, core/pdf.py)
-    "altair>=6.0",  # standard viz stack: Vega-Lite spec authoring; one spec renders to static SVG (vl-convert) AND interactive ui:// vega-embed panels. Light: pandas/numpy optional via narwhals.
-    "vl-convert-python>=1.6",  # Vega-Lite -> SVG/PNG, server-side (bundled Rust renderer; no browser/GDAL/chromium). BSD-3 + fully permissive dep closure (OFL font, BSD/MIT/ISC JS).
 ]
 
 [project.urls]
@@ -64,13 +60,12 @@ all = ["turnstone[discord,slack]"]
 
 [project.scripts]
 turnstone = "turnstone.cli:main"
-turnstone-eval = "turnstone.eval.cli:main"
-turnstone-optimizer = "turnstone.optimizer:main"
+turnstone-eval = "turnstone.eval:main"
 turnstone-server = "turnstone.server:main"
 turnstone-console = "turnstone.console.server:main"
 turnstone-admin = "turnstone.admin:main"
 turnstone-channel = "turnstone.channels.cli:main"
-turnstone-doctor = "turnstone.doctor:main"
+turnstone-bootstrap = "turnstone.bootstrap:main"
 
 [tool.hatch.build.targets.wheel]
 include = [
@@ -90,7 +85,7 @@ include = [
   "turnstone/shared_static/*.js",
   "turnstone/shared_static/katex-0.17.0/**/*",
   "turnstone/shared_static/hljs-11.11.1/**/*",
-  "turnstone/shared_static/mermaid-11.16.0/**/*",
+  "turnstone/shared_static/mermaid-11.15.0/**/*",
   "turnstone/shared_static/hls-1.6.16/**/*",
   "turnstone/sdk/py.typed",
   "turnstone/deploy/*.yaml",
@@ -194,10 +189,6 @@ ignore_missing_imports = true
 module = ["pypdfium2", "pypdfium2.*"]
 ignore_missing_imports = true
 
-[[tool.mypy.overrides]]
-module = ["vl_convert", "vl_convert.*"]  # Rust wheel, ships no type stubs (altair is typed)
-ignore_missing_imports = true
-
 [[tool.mypy.overrides]]
 module = ["turnstone.channels.discord.*"]
 disallow_subclassing_any = false

run.sh

@@ -369,7 +369,7 @@ ${GREEN}${BOLD}Turnstone is running${RESET} (${NODE_COUNT} node$([ "$NODE_COUNT"
     1. Create the first admin user:
        ${DIM}cd $INSTALL_DIR && $DOCKER compose exec node-1 turnstone-admin create-user --username admin --name "Admin"${RESET}
     2. Open ${url}, log in, and add a model backend in the ${BOLD}Models${RESET} tab —
-       a local server (vLLM / llama.cpp) or an OpenAI / Anthropic / Gemini key.
+       a local server (vLLM / llama.cpp / Ollama) or an OpenAI / Anthropic / Gemini key.
        Nodes boot without a model and pick it up live; no restart needed.
 
   Scale       Running ${scale}
@@ -380,9 +380,6 @@ ${GREEN}${BOLD}Turnstone is running${RESET} (${NODE_COUNT} node$([ "$NODE_COUNT"
               ${DIM}$DOCKER compose down${RESET}      stop (add -v to wipe data)
 
   Config      $INSTALL_DIR/.env   (generated secrets + ports)
-
-  Troubleshoot  ${DIM}pipx run --spec turnstone turnstone-doctor --dir $INSTALL_DIR${RESET}
-                LLM-backed diagnostics for this install (read-only; needs Python)
 EOF
 }
 

scripts/livepass.py

@@ -58,46 +58,6 @@ Attachments harness (/attachments/livepass.html): the composer attachment
   thumbnail crop/size, the native audio-control fit at the constrained
   height, the snippet contrast, and how a long filename behaves at the
   340px chip cap.
-Task-agent harness (/taskagent/livepass.html): the task_agent card — a task
-  agent's sub-tool steps nested under its conversation row, driven through the
-  REAL InteractivePane.handleEvent (parent tool_pending/tool_info -> child
-  tool_pending/tool_result/tool_output_chunk/approve_request -> task_agent
-  tool_result) so the SSE->card routing (_routeAgentItems / _ensureAgentCard,
-  and appendToolOutput finding the nested row by call_id) is exercised, not
-  just the leaf builders.  Query flags: &theme=light; &collapsed=1 (all-auto,
-  no approval -> the natural collapse-by-default state); &parallel=1 (card in a
-  2-tool batch, for the rail-bleed rules); &recall=1 (the RECALL path —
-  replayHistory rebuilding the card from a /history `agent_steps` overlay, i.e.
-  a reload while the ws is in memory); &expand=1 (open every card so a shot
-  shows the nested steps); &race=1 (child steps emitted BEFORE the task_agent
-  row paints — the parallel-pool ordering window; the orphan buffer must nest
-  them rather than let them escape to top-level); &orphan=1 (child steps whose
-  task_agent row NEVER paints — the safety valve must escape them to visible
-  top-level rows after the grace window, stamping TASKAGENT-ORPHANS-ESCAPED-<n>,
-  not leave them buffered/invisible).  document.title stamps
-  TASKAGENT-READY-<steps> on
-  success, TASKAGENT-FAILED-... / TASKAGENT-ERROR when routing breaks, so a
-  broken card can't screenshot green.
-
-Perf harness (/perf/livepass.html): long-session performance baseline for the
-  interactive pane — mounts the REAL InteractivePane at real scroll geometry
-  (fixed-height mount, production CSS chain) and drives production-shaped
-  events through pane.handleEvent/replayHistory with rAF yields, measuring:
-  replayHistory wall time at N messages, live event-storm cost per turn on top
-  of that transcript (reasoning/content deltas + tool batches + task_agent
-  cards), tool_output_chunk throughput, busy/idle churn, heap + node count +
-  _agentCards size across repeated replay cycles (leak probe), and longtask
-  counts.  Query params: ?n= (history size) &turns= &chunks= &cycles= &idle=
-  &post=1 (POST the JSON report to /perf/report — the --perf runner captures
-  it).  Results land in <pre id="perf-json"> and document.title stamps
-  PERF-READY-<n> / PERF-FAILED-<phase>.  MEASUREMENT RULES: never run with
-  --virtual-time-budget (it corrupts performance.now) and never pass
-  --force-prefers-reduced-motion (it disables the animations whose cost we
-  measure); the --perf runner passes --js-flags=--expose-gc and
-  --enable-precise-memory-info so heap numbers are stable and real.
-
-    python3 scripts/livepass.py --perf                  # 300 and 3000 msgs
-    python3 scripts/livepass.py --perf --perf-n 5000    # match the field run
 
 Rebuild after ANY markup change: the dialog blocks are embedded at build
 time. Assets are symlinked, so CSS/JS edits are live on refresh.
@@ -106,13 +66,7 @@ time. Assets are symlinked, so CSS/JS edits are live on refresh.
 from __future__ import annotations
 
 import argparse
-import http.server
-import json
 import re
-import shutil
-import subprocess
-import time
-import uuid
 from pathlib import Path
 
 ROOT = Path(__file__).resolve().parent.parent
@@ -813,547 +767,6 @@ ATTACH_TEMPLATE = """<!doctype html>
 """
 
 
-# --------------------------------------------------------------------------
-# Task-agent harness — the task_agent card: a task agent's sub-tool steps
-# nested under its conversation row.  Driven through the REAL
-# InteractivePane.handleEvent so the SSE->card ROUTING (_routeAgentItems /
-# _ensureAgentCard, plus appendToolOutput finding the nested row by call_id)
-# is exercised, not just the leaf builders.  The page frame is harness-only
-# chrome; the .conv-batch / task_agent card is what's under review.
-# --------------------------------------------------------------------------
-TASKAGENT_TEMPLATE = """<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1" />
-    <title>task_agent livepass</title>
-    <link rel="stylesheet" href="shared/base.css" />
-    <link rel="stylesheet" href="shared/ui-base.css" />
-    <link rel="stylesheet" href="shared/chat.css" />
-    <link rel="stylesheet" href="shared/conversation.css" />
-    <link rel="stylesheet" href="shared/cards.css" />
-    <link rel="stylesheet" href="shared/interactive.css" />
-    <style>
-      /* Harness-only framing (NOT under review) — a plausible pane context. */
-      body {
-        padding: 24px; margin: 0; background: var(--bg); color: var(--ink);
-        font-family: var(--font-sans, system-ui, sans-serif);
-      }
-      .demo-frame { max-width: 720px; margin: 0 auto; }
-      .demo-label {
-        font: 11px var(--font-mono, monospace); color: var(--ink-3);
-        text-transform: uppercase; letter-spacing: 0.08em; margin: 0 0 8px;
-      }
-    </style>
-  </head>
-  <body>
-    <div class="demo-frame">
-      <div class="demo-label">conversation — task_agent card (real InteractivePane.handleEvent)</div>
-      <div class="messages" id="messages"></div>
-    </div>
-    <script>
-      // interactive.js reads window.toast / window.authFetch; the static render
-      // never POSTs, so no-op stubs are enough.
-      window.toast = { error: function (m) { console.log("toast:", m); } };
-      window.authFetch = function () {
-        return Promise.resolve({
-          ok: true,
-          json: function () { return Promise.resolve({}); },
-          text: function () { return Promise.resolve(""); },
-        });
-      };
-    </script>
-    <script type="module">
-      import { InteractivePane } from "./shared/interactive.js";
-      const q = new URLSearchParams(location.search);
-      if (q.get("theme") === "light")
-        document.documentElement.dataset.theme = "light";
-
-      const messages = document.getElementById("messages");
-      try {
-        // Drive the REAL pane; stub only the host seams a mounted pane provides.
-        const pane = new InteractivePane("demo-ws");
-        pane.messagesEl = messages;
-        pane.inputEl = document.createElement("textarea");
-        pane.sendBtn = document.createElement("button");
-        pane.isNearBottom = () => false;
-        pane.scrollToBottom = () => {};
-        pane.removeEmptyState = () => {};
-        pane.removeThinkingIndicator = () => {};
-        pane.setBusy = () => {};
-        const ev = (e) => pane.handleEvent(e);
-
-        // ?recall=1: exercise the RECALL path — replayHistory rebuilding the
-        // card from the /history `agent_steps` overlay (a reload / reopen while
-        // the ws is still in memory), as opposed to the live SSE path below.
-        const recall = q.get("recall") === "1";
-        if (recall) {
-          pane.replayHistory([
-            { role: "user", content: "Find all call sites of resolve_alias and summarize them" },
-            { role: "assistant", tool_calls: [{
-              name: "task_agent", id: "task1",
-              arguments: JSON.stringify({ prompt: "Find call sites of resolve_alias" }),
-              agent_steps: [
-                { id: "task1::c1", name: "search", arguments: JSON.stringify({ query: "resolve_alias" }), output: "12 matches across 4 files", is_error: false },
-                { id: "task1::c2", name: "read_file", arguments: JSON.stringify({ path: "core/registry.py" }), output: "4.1 KB read", is_error: false },
-                { id: "task1::c3", name: "bash", arguments: JSON.stringify({ command: "pytest -k registry" }), output: "12 passed in 1.2s", is_error: false },
-                { id: "task1::c4", name: "notify", arguments: JSON.stringify({ channel: "#eng", message: "post summary" }), output: "posted to #eng", is_error: false },
-              ],
-            }] },
-            { role: "tool", tool_call_id: "task1", content: "resolve_alias has 4 call sites (registry.py:120, session.py:12200, model_registry.py:88, eval.py:54); all pass a validated alias before use." },
-          ]);
-        } else if (q.get("race") === "1") {
-          // ?race=1: reproduce the parallel-pool ordering window — each
-          // sub-tool's tool_pending is emitted exactly once (as in production)
-          // but AHEAD of the task_agent row paint, as happens when a pooled
-          // sub-agent's SSE event is handled before its parent row commits.
-          // The orphan buffer must hold them and nest them when the parent row
-          // lands; pre-fix they escaped to top-level rows and the card came up
-          // short (steps < 4 -> TASKAGENT-FAILED), so this can't screenshot
-          // green without the fix.
-          const raceTask = {
-            call_id: "task1", func_name: "task_agent",
-            header: 'task_agent: "Find all call sites of resolve_alias and summarize them"',
-            needs_approval: false,
-          };
-          const childPending = (cid, fn, header) =>
-            ev({ type: "tool_pending", items: [{ call_id: cid, parent_call_id: "task1", func_name: fn, header: header, needs_approval: false }] });
-          // a) Orphan child pendings arrive first — no parent row yet.
-          childPending("task1::c1", "search", 'search: "resolve_alias"');
-          childPending("task1::c2", "read_file", "read_file: core/registry.py");
-          childPending("task1::c3", "bash", "pytest -k registry");
-          childPending("task1::c4", "notify", "notify: post summary to #eng");
-          // b) Parent task_agent row paints (pending -> resolved): must flush the
-          //    buffered orphans into the card AND survive the upgrade rebuild.
-          ev({ type: "tool_pending", items: [raceTask] });
-          ev({ type: "tool_info", items: [Object.assign({ auto_approved: false }, raceTask)] });
-          // c) Results + a streamed chunk follow, nesting into the flushed rows.
-          ev({ type: "tool_result", call_id: "task1::c1", parent_call_id: "task1", name: "search", output: "12 matches across 4 files" });
-          ev({ type: "tool_result", call_id: "task1::c2", parent_call_id: "task1", name: "read_file", output: "4.1 KB read" });
-          ev({ type: "tool_output_chunk", call_id: "task1::c3", parent_call_id: "task1", chunk: "collected 12 items ... " });
-          ev({ type: "tool_result", call_id: "task1::c3", parent_call_id: "task1", name: "bash", output: "12 passed in 1.2s" });
-          ev({ type: "tool_result", call_id: "task1::c4", parent_call_id: "task1", name: "notify", output: "posted to #eng" });
-          ev({ type: "tool_result", call_id: "task1", name: "task_agent", output: "resolve_alias has 4 call sites (registry.py:120, session.py:12200, model_registry.py:88, eval.py:54); all pass a validated alias before use." });
-        } else if (q.get("orphan") === "1") {
-          // ?orphan=1: the SAFETY VALVE — child steps whose task_agent row
-          // NEVER paints (an id-correlation mismatch, or an agent aborted
-          // before its row painted).  They must not vanish: after the grace
-          // window the buffer escapes them to visible top-level rows (the
-          // pre-buffer behaviour) rather than holding them forever.  The parent
-          // task_agent row is deliberately never emitted here.
-          const orphanPending = (cid, fn, header) =>
-            ev({ type: "tool_pending", items: [{ call_id: cid, parent_call_id: "task1", func_name: fn, header: header, needs_approval: false }] });
-          orphanPending("task1::c1", "search", 'search: "resolve_alias"');
-          orphanPending("task1::c2", "read_file", "read_file: core/registry.py");
-          orphanPending("task1::c3", "bash", "pytest -k registry");
-        } else {
-
-        // 1. Parent paints the task_agent call (a top-level tool row).
-        const taskItem = {
-          call_id: "task1", func_name: "task_agent",
-          header: 'task_agent: "Find all call sites of resolve_alias and summarize them"',
-          needs_approval: false,
-        };
-        // ?parallel=1 puts the task_agent in a 2-tool parallel batch so the
-        // nested-step rail-bleed fix can be verified against the rail rules.
-        const parentItems = q.get("parallel") === "1"
-          ? [taskItem, { call_id: "sib1", func_name: "bash", header: "git status", needs_approval: false }]
-          : [taskItem];
-        ev({ type: "tool_pending", items: parentItems });
-        ev({ type: "tool_info", items: parentItems.map((it) => Object.assign({ auto_approved: false }, it)) });
-        if (parentItems.length > 1)
-          ev({ type: "tool_result", call_id: "sib1", name: "bash", output: "clean" });
-
-        // 2. Sub-agent steps tagged parent_call_id="task1" — exercises routing.
-        function stepRow(cid, fn, header, result) {
-          ev({ type: "tool_pending", items: [{ call_id: cid, parent_call_id: "task1", func_name: fn, header: header, needs_approval: false }] });
-          if (result != null)
-            ev({ type: "tool_result", call_id: cid, parent_call_id: "task1", name: fn, output: result });
-        }
-        stepRow("task1::c1", "search", 'search: "resolve_alias"', "12 matches across 4 files");
-        stepRow("task1::c2", "read_file", "read_file: core/registry.py", "4.1 KB read");
-        ev({ type: "tool_pending", items: [{ call_id: "task1::c3", parent_call_id: "task1", func_name: "bash", header: "pytest -k registry", needs_approval: false }] });
-        ev({ type: "tool_output_chunk", call_id: "task1::c3", parent_call_id: "task1", chunk: "collected 12 items ... " });
-        ev({ type: "tool_result", call_id: "task1::c3", parent_call_id: "task1", name: "bash", output: "12 passed in 1.2s" });
-        // 4th step.  Default: a nested sub-tool approval (notify is not
-        // auto-approved) — the pane must auto-expand the collapse-by-default
-        // card so the blocking prompt is visible.  ?collapsed=1: a plain
-        // completed step instead, so nothing forces the card open and the
-        // screenshot shows the natural collapsed state (the common case).
-        if (q.get("collapsed") === "1") {
-          stepRow("task1::c4", "notify", "notify: post summary to #eng", "posted to #eng");
-        } else {
-          ev({ type: "approve_request", judge_pending: false, items: [{ call_id: "task1::c4", parent_call_id: "task1", func_name: "notify", header: "notify: post summary to #eng", needs_approval: true }] });
-        }
-
-        // 3. The task agent's own synthesis, rendered below the card.
-        ev({ type: "tool_result", call_id: "task1", name: "task_agent", output: "resolve_alias has 4 call sites (registry.py:120, session.py:12200, model_registry.py:88, eval.py:54); all pass a validated alias before use." });
-        }
-
-        // ?expand=1: open every card so a screenshot shows the nested steps
-        // (cards collapse by default; recall has no approval to auto-expand).
-        if (q.get("expand") === "1") {
-          document.querySelectorAll(".conv-agent").forEach(function (c) {
-            c.dataset.collapsed = "false";
-            const t = c.querySelector(".conv-agent-toggle");
-            if (t) t.setAttribute("aria-expanded", "true");
-          });
-        }
-
-        // Loud failure — broken routing must not screenshot green.
-        const orphanMode = q.get("orphan") === "1";
-        setTimeout(function () {
-          if (orphanMode) {
-            // The parent never painted; after the grace window the buffered
-            // steps must have ESCAPED to visible top-level rows, not vanished.
-            const escaped = document.querySelectorAll('.conv-batch .conv-row[data-call-id^="task1::"]').length;
-            const leaked = document.querySelector('.conv-row[data-call-id="task1"] .conv-agent');
-            document.title = escaped >= 3 && !leaked
-              ? "TASKAGENT-ORPHANS-ESCAPED-" + escaped
-              : "TASKAGENT-FAILED-escaped" + escaped + "-card" + (leaked ? 1 : 0);
-            return;
-          }
-          const row = document.querySelector('.conv-row[data-call-id="task1"]');
-          const card = row && row.querySelector(".conv-agent");
-          const steps = card ? card.querySelectorAll(".conv-agent-body .conv-row").length : 0;
-          const hasResult = !!(row && /call sites/.test(row.textContent || ""));
-          document.title = card && steps >= 4 && hasResult
-            ? "TASKAGENT-READY-" + steps
-            : "TASKAGENT-FAILED-card" + (card ? 1 : 0) + "-steps" + steps + "-result" + (hasResult ? 1 : 0);
-        }, orphanMode ? 900 : 300);
-      } catch (e) {
-        messages.textContent = "HARNESS ERROR: " + e.message + "\\n" + (e.stack || "");
-        document.title = "TASKAGENT-ERROR";
-      }
-    </script>
-  </body>
-</html>
-"""
-
-
-# --------------------------------------------------------------------------
-# Perf harness — long-session performance baseline for the interactive pane.
-# Mounts the REAL InteractivePane (production DOM via _createDOM, production
-# CSS chain) in a fixed-height mount so .pane-messages has REAL scroll
-# geometry — the forced-layout costs under measurement (isNearBottom /
-# scrollToBottom / chunk-append scroll pins) only exist against live layout,
-# which is why nothing here stubs scroll/geometry the way the task-agent
-# harness does.  All timing is real time (see MEASUREMENT RULES in the module
-# docstring).  Workload is deterministic (seeded LCG) so runs are comparable.
-# --------------------------------------------------------------------------
-PERF_TEMPLATE = """<!doctype html>
-<html lang="en">
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1" />
-    <title>perf livepass</title>
-    <link rel="stylesheet" href="shared/base.css" />
-    <link rel="stylesheet" href="shared/ui-base.css" />
-    <link rel="stylesheet" href="shared/chat.css" />
-    <link rel="stylesheet" href="shared/conversation.css" />
-    <link rel="stylesheet" href="shared/cards.css" />
-    <link rel="stylesheet" href="static/style.css" />
-    <link rel="stylesheet" href="shared/interactive.css" />
-    <style>
-      /* Harness-only framing (NOT under review): a fixed-height mount so the
-         pane's .pane-messages scroller has real production geometry. */
-      body { margin: 0; background: var(--bg); color: var(--fg); }
-      #mount { height: 720px; width: 920px; display: flex; overflow: hidden; }
-      #mount > .pane { flex: 1; display: flex; flex-direction: column; min-height: 0; }
-      #perf-json { font: 11px monospace; white-space: pre-wrap; padding: 12px; }
-    </style>
-  </head>
-  <body>
-    <div id="mount"></div>
-    <pre id="perf-json">running…</pre>
-    <script>
-      window.toast = { error: function (m) { console.log("toast:", m); } };
-      // Collect every uncaught error/rejection into the report — a perf run
-      // that silently swallowed a pipeline exception must not read as clean.
-      window.__perfErrors = [];
-      window.onerror = function (msg, src, line) {
-        window.__perfErrors.push(String(msg) + " @ " + (src || "?") + ":" + (line || 0));
-      };
-      window.addEventListener("unhandledrejection", function (e) {
-        window.__perfErrors.push("unhandledrejection: " + String(e && e.reason));
-      });
-      window.__perfFetch = function () {
-        return Promise.resolve({
-          ok: true, status: 200,
-          json: function () { return Promise.resolve({}); },
-          text: function () { return Promise.resolve(""); },
-        });
-      };
-      window.authFetch = window.__perfFetch;
-    </script>
-    <script type="module">
-      import { InteractivePane } from "./shared/interactive.js";
-      // auth.js's legacy window bridge clobbers window.authFetch at module
-      // import time — reinstate the stub now imports have evaluated (same
-      // dance as the attachments harness).
-      window.authFetch = window.__perfFetch;
-
-      const q = new URLSearchParams(location.search);
-      const N = parseInt(q.get("n") || "1000", 10);
-      const TURNS = parseInt(q.get("turns") || "20", 10);
-      const CHUNKS = parseInt(q.get("chunks") || "300", 10);
-      const CYCLES = parseInt(q.get("cycles") || "3", 10);
-      const IDLE = parseInt(q.get("idle") || "20", 10);
-
-      // Long-task accounting across every phase (>50ms main-thread blocks).
-      const lt = { count: 0, total_ms: 0, max_ms: 0 };
-      try {
-        new PerformanceObserver(function (list) {
-          list.getEntries().forEach(function (e) {
-            lt.count += 1;
-            lt.total_ms += Math.round(e.duration);
-            lt.max_ms = Math.max(lt.max_ms, Math.round(e.duration));
-          });
-        }).observe({ type: "longtask", buffered: true });
-      } catch (e) { /* unsupported — longtasks stay zeroed */ }
-
-      // Deterministic workload (seeded LCG) so runs are comparable.
-      let _seed = 42;
-      function rnd() {
-        _seed = (_seed * 1664525 + 1013904223) >>> 0;
-        return _seed / 4294967296;
-      }
-      const WORDS = ("the retry loop grinds the dungeon server while the " +
-        "judge weighs verdicts and the coordinator shuffles children across " +
-        "nodes tokens accumulate compaction folds turns storage keeps the " +
-        "canon and the rail repaints").split(" ");
-      function sentence(w) {
-        const parts = [];
-        for (let i = 0; i < w; i++) parts.push(WORDS[(rnd() * WORDS.length) | 0]);
-        return parts.join(" ");
-      }
-      // Realistic assistant markdown: prose + list + fenced code (varying
-      // content so the hljs cache behaves as in production) + inline code.
-      function mdBody(i) {
-        return (
-          "Turn " + i + ": " + sentence(18) + ".\\n\\n" +
-          "- " + sentence(6) + "\\n- " + sentence(7) + "\\n\\n" +
-          "```python\\n" +
-          "def step_" + i + "(depth):\\n" +
-          "    total = " + ((rnd() * 1000) | 0) + "\\n" +
-          "    for k in range(depth):\\n" +
-          "        total += k * " + (1 + ((rnd() * 9) | 0)) + "\\n" +
-          "    return total\\n" +
-          "```\\n\\n" +
-          sentence(14) + " `inline_" + i + "` " + sentence(8) + "."
-        );
-      }
-      // History in the canonical projected wire shape replayHistory consumes
-      // (user / assistant content / assistant tool_calls / tool result), with
-      // periodic reasoning bubbles and task_agent cards (agent_steps overlay).
-      function buildHistory(n) {
-        const msgs = [];
-        let i = 0;
-        while (msgs.length < n) {
-          i += 1;
-          msgs.push({ role: "user", content: "Request " + i + ": " + sentence(10) + "?" });
-          if (msgs.length >= n) break;
-          if (i % 10 === 0) {
-            msgs.push({ role: "assistant", reasoning: sentence(40) + ".", content: mdBody(i) });
-          } else {
-            msgs.push({ role: "assistant", content: mdBody(i) });
-          }
-          if (msgs.length >= n) break;
-          const callId = "h" + i;
-          if (i % 8 === 0) {
-            msgs.push({ role: "assistant", tool_calls: [{
-              name: "task_agent", id: callId,
-              arguments: JSON.stringify({ prompt: "subtask " + i }),
-              agent_steps: [
-                { id: callId + "::c1", name: "search",
-                  arguments: JSON.stringify({ query: "q" + i }),
-                  output: sentence(8), is_error: false },
-                { id: callId + "::c2", name: "read_file",
-                  arguments: JSON.stringify({ path: "core/f" + i + ".py" }),
-                  output: sentence(6), is_error: false },
-                { id: callId + "::c3", name: "bash",
-                  arguments: JSON.stringify({ command: "pytest -k t" + i }),
-                  output: sentence(7), is_error: false },
-              ],
-            }] });
-          } else {
-            msgs.push({ role: "assistant", tool_calls: [{
-              name: "bash", id: callId,
-              arguments: JSON.stringify({ command: "grep -rn pattern_" + i + " src/" }),
-            }] });
-          }
-          if (msgs.length >= n) break;
-          msgs.push({ role: "tool", tool_call_id: callId,
-            content: "output " + i + ":\\n" + sentence(20) });
-        }
-        return msgs;
-      }
-
-      const tick = () => new Promise((r) => requestAnimationFrame(r));
-      // One live turn, production event mix: thinking indicator, reasoning
-      // deltas, content deltas (yield every few so streamingRender's internal
-      // rAF actually applies frames, as in a real token stream), stream_end,
-      // an auto-approved bash batch with streamed chunks, every 5th turn a
-      // task_agent card with routed children, then the idle edge.
-      async function stormTurn(pane, i) {
-        pane.handleEvent({ type: "state_change", state: "running" });
-        pane.handleEvent({ type: "thinking_start" });
-        const reason = sentence(50);
-        let d = 0;
-        for (let k = 0; k < reason.length; k += 20) {
-          pane.handleEvent({ type: "reasoning", text: reason.slice(k, k + 20) });
-          d += 1;
-          if (d % 4 === 3) await tick();
-        }
-        const body = mdBody(100000 + i);
-        d = 0;
-        for (let k = 0; k < body.length; k += 22) {
-          pane.handleEvent({ type: "content", text: body.slice(k, k + 22) });
-          d += 1;
-          if (d % 6 === 5) await tick();
-        }
-        pane.handleEvent({ type: "stream_end" });
-        const callId = "s" + i;
-        const item = { call_id: callId, func_name: "bash",
-          header: "bash: run step " + i, needs_approval: false };
-        pane.handleEvent({ type: "tool_pending", items: [item] });
-        pane.handleEvent({ type: "tool_info",
-          items: [Object.assign({ auto_approved: true }, item)] });
-        for (let k = 0; k < 24; k++) {
-          pane.handleEvent({ type: "tool_output_chunk", call_id: callId,
-            chunk: "line " + k + ": " + sentence(5) + "\\n" });
-          if (k % 6 === 5) await tick();
-        }
-        pane.handleEvent({ type: "tool_result", call_id: callId, name: "bash",
-          output: "done " + i + "\\n" + sentence(12) });
-        if (i % 5 === 4) {
-          const tid = "sa" + i;
-          const titem = { call_id: tid, func_name: "task_agent",
-            header: 'task_agent: "subtask ' + i + '"', needs_approval: false };
-          pane.handleEvent({ type: "tool_pending", items: [titem] });
-          pane.handleEvent({ type: "tool_info",
-            items: [Object.assign({ auto_approved: true }, titem)] });
-          for (let c = 1; c <= 3; c++) {
-            const cid = tid + "::c" + c;
-            pane.handleEvent({ type: "tool_pending", items: [{
-              call_id: cid, parent_call_id: tid, func_name: "search",
-              header: "search: q" + c, needs_approval: false }] });
-            pane.handleEvent({ type: "tool_result", call_id: cid,
-              parent_call_id: tid, name: "search", output: sentence(6) });
-          }
-          pane.handleEvent({ type: "tool_result", call_id: tid,
-            name: "task_agent", output: sentence(15) });
-          await tick();
-        }
-        pane.handleEvent({ type: "state_change", state: "idle" });
-        await tick();
-      }
-
-      function heapBytes() {
-        // --js-flags=--expose-gc makes this a real floor, not GC noise.
-        if (typeof window.gc === "function") {
-          try { window.gc(); window.gc(); } catch (e) { /* noop */ }
-        }
-        return (performance.memory && performance.memory.usedJSHeapSize) || null;
-      }
-
-      const report = {
-        n: N, turns: TURNS, chunks: CHUNKS, cycles: CYCLES, idle: IDLE,
-        // Echoed run token — the runner validates it so a straggler POST
-        // from a killed prior attempt can't be misattributed to this run.
-        run: q.get("run") || "",
-        errors: window.__perfErrors,
-      };
-      let phase = "mount";
-      try {
-        const pane = new InteractivePane("perf-ws");
-        document.getElementById("mount").appendChild(pane.el);
-        const msgs = buildHistory(N);
-        report.heap_start = heapBytes();
-
-        phase = "replay";
-        let t0 = performance.now();
-        pane.replayHistory(msgs);
-        report.replay_ms = Math.round(performance.now() - t0);
-        await tick();
-        report.nodes_after_replay = pane.messagesEl.querySelectorAll("*").length;
-
-        phase = "storm";
-        t0 = performance.now();
-        for (let i = 0; i < TURNS; i++) await stormTurn(pane, i);
-        report.storm_ms = Math.round(performance.now() - t0);
-        report.storm_ms_per_turn = Math.round(report.storm_ms / TURNS);
-
-        phase = "chunkstorm";
-        const ccItem = { call_id: "cc1", func_name: "bash",
-          header: "bash: tail -f build.log", needs_approval: false };
-        pane.handleEvent({ type: "tool_pending", items: [ccItem] });
-        pane.handleEvent({ type: "tool_info",
-          items: [Object.assign({ auto_approved: true }, ccItem)] });
-        t0 = performance.now();
-        for (let k = 0; k < CHUNKS; k++) {
-          pane.handleEvent({ type: "tool_output_chunk", call_id: "cc1",
-            chunk: "log line " + k + "\\n" });
-          if (k % 6 === 5) await tick();
-        }
-        report.chunk_ms = Math.round(performance.now() - t0);
-        pane.handleEvent({ type: "tool_result", call_id: "cc1", name: "bash",
-          output: "tail done" });
-
-        phase = "idlechurn";
-        t0 = performance.now();
-        for (let k = 0; k < IDLE; k++) {
-          pane.handleEvent({ type: "state_change", state: "running" });
-          pane.handleEvent({ type: "state_change", state: "idle" });
-          if (k % 4 === 3) await tick();
-        }
-        report.idle_ms = Math.round(performance.now() - t0);
-
-        // Leak probe: repeated full replays of the SAME history should
-        // converge to a flat heap/node/agent-card profile; monotonic growth
-        // here is retained-detached-DOM (the _agentCards class of bug).
-        phase = "replaycycles";
-        report.cycle_stats = [];
-        for (let c = 0; c < CYCLES; c++) {
-          t0 = performance.now();
-          pane.replayHistory(msgs);
-          const ms = Math.round(performance.now() - t0);
-          await tick();
-          report.cycle_stats.push({
-            replay_ms: ms,
-            heap: heapBytes(),
-            nodes: pane.messagesEl.querySelectorAll("*").length,
-            agent_cards: pane._agentCards ? pane._agentCards.size : 0,
-          });
-        }
-        report.heap_end = heapBytes();
-        report.longtasks = lt;
-        document.title = "PERF-READY-" + N;
-      } catch (e) {
-        window.__perfErrors.push(
-          "phase " + phase + ": " + (e && e.message ? e.message : String(e)),
-        );
-        report.failed_phase = phase;
-        report.longtasks = lt;
-        document.title = "PERF-FAILED-" + phase;
-      }
-      document.getElementById("perf-json").textContent =
-        JSON.stringify(report, null, 2);
-      if (q.get("post")) {
-        try {
-          await fetch("/perf/report", {
-            method: "POST",
-            headers: { "Content-Type": "application/json" },
-            body: JSON.stringify(report),
-          });
-        } catch (e) { /* runner captures the timeout instead */ }
-      }
-    </script>
-  </body>
-</html>
-"""
-
-
 # Fixture media for the attachments harness.  image/pdf thumbnails and the
 # audio clip load via element .src (NOT authFetch), so the --serve dev server
 # answers those paths directly with representative bytes: a photo-like image,
@@ -1470,266 +883,34 @@ def build(out: Path) -> None:
     (att / "livepass.html").write_text(ATTACH_TEMPLATE, encoding="utf-8")
     print(f"{att}/livepass.html — composer chips + message attachment pills")
 
-    ta = out / "taskagent"
-    ta.mkdir(parents=True, exist_ok=True)
-    symlink(ta / "shared", ROOT / "turnstone/shared_static")
-    (ta / "livepass.html").write_text(TASKAGENT_TEMPLATE, encoding="utf-8")
-    print(f"{ta}/livepass.html — task_agent card (real Pane.handleEvent routing)")
-
-    pf = out / "perf"
-    pf.mkdir(parents=True, exist_ok=True)
-    symlink(pf / "shared", ROOT / "turnstone/shared_static")
-    symlink(pf / "static", ROOT / "turnstone/ui/static")
-    (pf / "livepass.html").write_text(PERF_TEMPLATE, encoding="utf-8")
-    print(f"{pf}/livepass.html — long-session perf baseline (real InteractivePane)")
-
-
-class _PerfStore:
-    """Rendezvous for the perf page's POSTed JSON report."""
-
-    def __init__(self) -> None:
-        import threading
-
-        self.event = threading.Event()
-        self.data: dict[str, object] | None = None
-
-
-class _HarnessHandler(http.server.SimpleHTTPRequestHandler):
-    """Static file server + attachment media fixtures + perf-report sink.
-
-    The attachments harness loads thumbnails + the audio clip via element
-    .src; serve those from generated fixtures, fall through to static for
-    everything else.  The perf harness POSTs its JSON report to /perf/report
-    when driven with ?post=1 — the --perf runner blocks on ``perf_store``.
-    """
-
-    perf_store: _PerfStore | None = None
-    quiet = False
-
-    def do_GET(self) -> None:  # noqa: N802 (stdlib casing)
-        blob = _fixture_for(self.path.split("?")[0])
-        if blob is None:
-            super().do_GET()
-            return
-        data, ctype = blob
-        self.send_response(200)
-        self.send_header("Content-Type", ctype)
-        self.send_header("Content-Length", str(len(data)))
-        self.end_headers()
-        self.wfile.write(data)
-
-    def do_POST(self) -> None:  # noqa: N802 (stdlib casing)
-        store = type(self).perf_store
-        if self.path.split("?")[0] != "/perf/report" or store is None:
-            self.send_error(404)
-            return
-        length = int(self.headers.get("Content-Length") or 0)
-        body = self.rfile.read(length)
-        try:
-            store.data = json.loads(body)
-        except ValueError:
-            store.data = {"errors": ["runner: unparseable report body"]}
-        store.event.set()
-        self.send_response(204)
-        self.end_headers()
-
-    def log_message(self, format: str, *args: object) -> None:  # noqa: A002 (stdlib signature)
-        if not type(self).quiet:
-            super().log_message(format, *args)
-
-
-def _find_chrome() -> str | None:
-    for name in ("google-chrome", "google-chrome-stable", "chromium", "chromium-browser"):
-        path = shutil.which(name)
-        if path:
-            return path
-    return None
-
-
-def _await_report(
-    store: _PerfStore, proc: subprocess.Popen[bytes], run_token: str, timeout: float
-) -> dict[str, object] | None:
-    """Wait for THIS attempt's report: validated by run token, bailing early
-    when Chrome exits without reporting (the sandbox-startup-failure case —
-    waiting the full timeout there cost minutes before the --no-sandbox
-    fallback could even start).  A straggler POST from a previous attempt
-    (its handler thread can complete after the next attempt cleared the
-    store) carries the wrong token and is discarded instead of being
-    misattributed to this run."""
-    deadline = time.monotonic() + timeout
-    proc_exited_at: float | None = None
-    while time.monotonic() < deadline:
-        if store.event.wait(0.5):
-            data = store.data
-            store.event.clear()
-            store.data = None
-            if isinstance(data, dict) and data.get("run") == run_token:
-                return data
-            continue  # stale straggler from a prior attempt — keep waiting
-        if proc.poll() is not None:
-            now = time.monotonic()
-            if proc_exited_at is None:
-                proc_exited_at = now  # grace: an in-flight POST may still land
-            elif now - proc_exited_at > 3.0:
-                return None  # exited without reporting — try the next attempt
-    return None
-
-
-def _perf_run_one(
-    chrome: str, out: Path, port: int, store: _PerfStore, n: int, turns: int, timeout: float
-) -> dict[str, object] | None:
-    """One headless-Chrome perf pass; returns the page's report or None."""
-    base_flags = [
-        "--headless=new",
-        "--disable-gpu",
-        "--hide-scrollbars",
-        "--window-size=1440,900",
-        "--no-first-run",
-        "--disable-extensions",
-        # Throttled timers/rAF in a backgrounded renderer would corrupt the
-        # measurement — pin the renderer foreground-scheduled.
-        "--disable-background-timer-throttling",
-        "--disable-renderer-backgrounding",
-        "--disable-backgrounding-occluded-windows",
-        # Stable, real heap numbers (heapBytes() calls window.gc() first).
-        "--js-flags=--expose-gc",
-        "--enable-precise-memory-info",
-    ]
-    for attempt, extra in enumerate(
-        ([], ["--no-sandbox"])  # sandboxed first, container fallback second
-    ):
-        run_token = f"n{n}-a{attempt}-{uuid.uuid4().hex[:8]}"
-        url = (
-            f"http://127.0.0.1:{port}/perf/livepass.html?n={n}&turns={turns}&post=1&run={run_token}"
-        )
-        store.event.clear()
-        store.data = None
-        profile = out / f".chrome-perf-{n}"
-        proc = subprocess.Popen(
-            [chrome, *base_flags, *extra, f"--user-data-dir={profile}", url],
-            stdout=subprocess.DEVNULL,
-            stderr=subprocess.DEVNULL,
-        )
-        try:
-            report = _await_report(store, proc, run_token, timeout)
-            if report is not None:
-                return report
-        finally:
-            if proc.poll() is None:
-                proc.terminate()
-                try:
-                    proc.wait(10)
-                except subprocess.TimeoutExpired:
-                    proc.kill()
-    return None
-
-
-def run_perf(out: Path, sizes: list[int], turns: int, timeout: float) -> bool:
-    """Build, serve, and run the perf page once per history size; print a table."""
-    import functools
-    import threading
-
-    chrome = _find_chrome()
-    if chrome is None:
-        print("perf: no chrome/chromium binary found on PATH")
-        return False
-    store = _PerfStore()
-    _HarnessHandler.perf_store = store
-    _HarnessHandler.quiet = True
-    handler = functools.partial(_HarnessHandler, directory=str(out))
-    server = http.server.ThreadingHTTPServer(("127.0.0.1", 0), handler)
-    port = server.server_address[1]
-    threading.Thread(target=server.serve_forever, daemon=True).start()
-    reports: dict[int, dict[str, object]] = {}
-    try:
-        for n in sizes:
-            print(f"perf: n={n} turns={turns} … ", end="", flush=True)
-            report = _perf_run_one(chrome, out, port, store, n, turns, timeout)
-            if report is None:
-                print("FAILED (no report — timeout or chrome startup failure)")
-                continue
-            failed = report.get("failed_phase")
-            errors = report.get("errors") or []
-            status = f"failed in {failed}" if failed else "ok"
-            print(f"{status} ({len(errors) if isinstance(errors, list) else '?'} page errors)")
-            reports[n] = report
-            (out / f"perf-report-n{n}.json").write_text(
-                json.dumps(report, indent=2), encoding="utf-8"
-            )
-    finally:
-        server.shutdown()
-        _HarnessHandler.perf_store = None
-        _HarnessHandler.quiet = False
-    if not reports:
-        return False
-    _print_perf_table(reports)
-    print(f"\nraw reports: {out}/perf-report-n*.json")
-    return True
-
-
-def _print_perf_table(reports: dict[int, dict[str, object]]) -> None:
-    sizes = sorted(reports)
-
-    def cell(n: int, key: str) -> str:
-        value = reports[n].get(key)
-        return "—" if value is None else str(value)
-
-    def mb(value: object) -> str:
-        return f"{value / 1048576:.1f}MB" if isinstance(value, (int, float)) else "—"
-
-    rows: list[tuple[str, list[str]]] = [
-        ("replay_ms (full history build)", [cell(n, "replay_ms") for n in sizes]),
-        ("nodes after replay", [cell(n, "nodes_after_replay") for n in sizes]),
-        ("storm ms/turn (live mix)", [cell(n, "storm_ms_per_turn") for n in sizes]),
-        ("chunk_ms (output chunks)", [cell(n, "chunk_ms") for n in sizes]),
-        ("idle_ms (busy/idle churn)", [cell(n, "idle_ms") for n in sizes]),
-        ("heap start → end", []),
-        ("longtasks count/max_ms", []),
-        ("replay cycles ms", []),
-        ("agent_cards after cycles", []),
-    ]
-    for n in sizes:
-        rep = reports[n]
-        rows[5][1].append(f"{mb(rep.get('heap_start'))} → {mb(rep.get('heap_end'))}")
-        lt = rep.get("longtasks")
-        rows[6][1].append(f"{lt.get('count')}/{lt.get('max_ms')}" if isinstance(lt, dict) else "—")
-        cycles = rep.get("cycle_stats")
-        if isinstance(cycles, list) and cycles:
-            rows[7][1].append(",".join(str(c.get("replay_ms", "?")) for c in cycles))
-            rows[8][1].append(str(cycles[-1].get("agent_cards", "?")))
-        else:
-            rows[7][1].append("—")
-            rows[8][1].append("—")
-
-    label_w = max(len(label) for label, _ in rows)
-    col_w = max(14, *(len(f"n={n}") for n in sizes))
-    header = " " * label_w + "  " + "  ".join(f"n={n}".rjust(col_w) for n in sizes)
-    print("\n" + header)
-    for label, cells in rows:
-        print(label.ljust(label_w) + "  " + "  ".join(c.rjust(col_w) for c in cells))
-
 
 def main() -> None:
     ap = argparse.ArgumentParser(description=__doc__.splitlines()[0])
     ap.add_argument("--out", type=Path, default=Path("/tmp/livepass"))
     ap.add_argument("--serve", type=int, metavar="PORT")
-    ap.add_argument("--perf", action="store_true", help="run the perf baseline and exit")
-    ap.add_argument(
-        "--perf-n",
-        default="300,3000",
-        help="comma-separated history sizes for --perf (default: 300,3000)",
-    )
-    ap.add_argument("--perf-turns", type=int, default=20)
-    ap.add_argument("--perf-timeout", type=float, default=420.0)
     args = ap.parse_args()
     build(args.out)
-    if args.perf:
-        sizes = [int(s) for s in str(args.perf_n).split(",") if s.strip()]
-        raise SystemExit(0 if run_perf(args.out, sizes, args.perf_turns, args.perf_timeout) else 1)
     if args.serve:
         import functools
+        import http.server
 
-        handler = functools.partial(_HarnessHandler, directory=str(args.out))
+        class _FixtureHandler(http.server.SimpleHTTPRequestHandler):
+            # The attachments harness loads thumbnails + the audio clip via
+            # element .src; serve those from generated fixtures, fall through
+            # to static for everything else.
+            def do_GET(self) -> None:  # noqa: N802 (stdlib casing)
+                blob = _fixture_for(self.path.split("?")[0])
+                if blob is None:
+                    super().do_GET()
+                    return
+                data, ctype = blob
+                self.send_response(200)
+                self.send_header("Content-Type", ctype)
+                self.send_header("Content-Length", str(len(data)))
+                self.end_headers()
+                self.wfile.write(data)
+
+        handler = functools.partial(_FixtureHandler, directory=str(args.out))
         print(f"serving {args.out} on http://localhost:{args.serve}/ — Ctrl+C stops")
         http.server.ThreadingHTTPServer(("127.0.0.1", args.serve), handler).serve_forever()
 

sdk/typescript/openapi-console.json

@@ -2,7 +2,7 @@
   "openapi": "3.1.0",
   "info": {
     "title": "turnstone Console API",
-    "version": "1.7.0a6",
+    "version": "1.6.0a6",
     "description": "Cluster-wide visibility and control across all turnstone nodes."
   },
   "paths": {
@@ -3955,47 +3955,6 @@
         }
       }
     },
-    "/v1/api/admin/model-definitions/{definition_id}/calibrate": {
-      "post": {
-        "summary": "Calibrate a reranker model definition and persist its per-model floor",
-        "operationId": "v1_api_admin_model-definitions_{definition_id}_calibrate_post",
-        "tags": [
-          "Admin"
-        ],
-        "parameters": [
-          {
-            "name": "definition_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string"
-            }
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Success",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/CalibrateModelResponse"
-                }
-              }
-            }
-          },
-          "404": {
-            "description": "Error 404",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
     "/v1/api/admin/model-capabilities": {
       "get": {
         "summary": "Look up static capabilities for a known model",
@@ -4213,166 +4172,6 @@
         }
       }
     },
-    "/v1/api/admin/personas": {
-      "get": {
-        "summary": "List all personas, archived included",
-        "operationId": "v1_api_admin_personas_get",
-        "tags": [
-          "Admin"
-        ],
-        "responses": {
-          "200": {
-            "description": "Success",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ListPersonasResponse"
-                }
-              }
-            }
-          }
-        }
-      },
-      "post": {
-        "summary": "Create a persona",
-        "operationId": "v1_api_admin_personas_post",
-        "tags": [
-          "Admin"
-        ],
-        "requestBody": {
-          "required": true,
-          "content": {
-            "application/json": {
-              "schema": {
-                "$ref": "#/components/schemas/CreatePersonaRequest"
-              }
-            }
-          }
-        },
-        "responses": {
-          "200": {
-            "description": "Success",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/PersonaInfo"
-                }
-              }
-            }
-          },
-          "400": {
-            "description": "Error 400",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
-    "/v1/api/admin/personas/{persona_id}": {
-      "get": {
-        "summary": "Get a single persona",
-        "operationId": "v1_api_admin_personas_{persona_id}_get",
-        "tags": [
-          "Admin"
-        ],
-        "parameters": [
-          {
-            "name": "persona_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string"
-            }
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Success",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/PersonaInfo"
-                }
-              }
-            }
-          },
-          "404": {
-            "description": "Error 404",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          }
-        }
-      },
-      "patch": {
-        "summary": "Update a persona (edit levers, archive/unarchive, flip default)",
-        "operationId": "v1_api_admin_personas_{persona_id}_patch",
-        "tags": [
-          "Admin"
-        ],
-        "parameters": [
-          {
-            "name": "persona_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string"
-            }
-          }
-        ],
-        "requestBody": {
-          "required": true,
-          "content": {
-            "application/json": {
-              "schema": {
-                "$ref": "#/components/schemas/UpdatePersonaRequest"
-              }
-            }
-          }
-        },
-        "responses": {
-          "200": {
-            "description": "Success",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/PersonaInfo"
-                }
-              }
-            }
-          },
-          "400": {
-            "description": "Error 400",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          },
-          "404": {
-            "description": "Error 404",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
     "/v1/api/admin/node-metadata": {
       "get": {
         "summary": "Get metadata for all nodes (bulk)",
@@ -5290,7 +5089,7 @@
         "tags": [
           "Coordinator"
         ],
-        "description": "Worker thread picks up the message via the session's queue. Optional ``attachment_ids`` select staged uploads to attach to the message (parity with the interactive surface). Response carries ``attached_ids`` / ``dropped_attachment_ids`` so callers can detect partial attaches and ``priority`` / ``msg_id`` on the queued path. ``status: queue_full`` when the worker queue is full \u2014 caller should back off.",
+        "description": "Worker thread picks up the message via the session's queue. Optional ``attachment_ids`` reserve attachments under the message's send_id token (parity with the interactive surface). Response carries ``attached_ids`` / ``dropped_attachment_ids`` so callers can detect partial reservations and ``priority`` / ``msg_id`` on the queued path. ``status: queue_full`` when the worker queue is full \u2014 caller should back off.",
         "parameters": [
           {
             "name": "ws_id",
@@ -5392,7 +5191,7 @@
         "tags": [
           "Coordinator"
         ],
-        "description": "Multipart upload (field ``file``). Same validation rules as the interactive surface: magic-byte image sniff, UTF-8 text decode, per-kind size cap, per-(ws,user) pending cap. Attachments stay pending until a subsequent ``/send`` attaches them to a message.",
+        "description": "Multipart upload (field ``file``). Same validation rules as the interactive surface: magic-byte image sniff, UTF-8 text decode, per-kind size cap, per-(ws,user) pending cap. Attachments stay pending until a subsequent ``/send`` reserves them under its ``send_id`` token.",
         "parameters": [
           {
             "name": "ws_id",
@@ -6206,81 +6005,6 @@
         }
       }
     },
-    "/v1/api/workstreams/{ws_id}/export": {
-      "get": {
-        "summary": "Export the coordinator's conversation as OpenAI messages JSON",
-        "operationId": "v1_api_workstreams_{ws_id}_export_get",
-        "tags": [
-          "Coordinator"
-        ],
-        "description": "Returns the coordinator's own conversation as an ``{\"messages\": [...]}`` OpenAI Chat Completions envelope, served as a ``<ws_id>.json`` file download. Conversation-only (children are not bundled over HTTP). Gated on ``admin.coordinator``.",
-        "parameters": [
-          {
-            "name": "ws_id",
-            "in": "path",
-            "required": true,
-            "schema": {
-              "type": "string"
-            }
-          }
-        ],
-        "responses": {
-          "200": {
-            "description": "Success"
-          },
-          "400": {
-            "description": "Error 400",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          },
-          "403": {
-            "description": "Error 403",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          },
-          "404": {
-            "description": "Error 404",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          },
-          "500": {
-            "description": "Error 500",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          },
-          "503": {
-            "description": "Error 503",
-            "content": {
-              "application/json": {
-                "schema": {
-                  "$ref": "#/components/schemas/ErrorResponse"
-                }
-              }
-            }
-          }
-        }
-      }
-    },
     "/v1/api/workstreams/{ws_id}/children": {
       "get": {
         "summary": "List the coordinator's spawned child workstreams",
@@ -6599,6 +6323,78 @@
         }
       }
     },
+    "/v1/api/workstreams/{ws_id}/stop_cascade": {
+      "post": {
+        "summary": "Cancel the coordinator and every direct child",
+        "operationId": "v1_api_workstreams_{ws_id}_stop_cascade_post",
+        "tags": [
+          "Coordinator"
+        ],
+        "description": "Cancels the coordinator's in-flight generation AND dispatches ``cancel_workstream`` through the routing proxy for every direct child in the in-memory registry.  Grandchildren are not touched directly \u2014 they sit behind their parent's cancel, which propagates via the child's SSE stream.  Returns the per-child disposition (``cancelled`` / ``failed``) so the UI can show which children responded.  Writes ``coordinator.stopped_cascade`` with the two lists.",
+        "parameters": [
+          {
+            "name": "ws_id",
+            "in": "path",
+            "required": true,
+            "schema": {
+              "type": "string"
+            }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "Success",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/CoordinatorStopCascadeResponse"
+                }
+              }
+            }
+          },
+          "400": {
+            "description": "Error 400",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          },
+          "403": {
+            "description": "Error 403",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          },
+          "404": {
+            "description": "Error 404",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          },
+          "503": {
+            "description": "Error 503",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "$ref": "#/components/schemas/ErrorResponse"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/v1/api/workstreams/{ws_id}/close_all_children": {
       "post": {
         "summary": "Soft-close every direct child of the coordinator",
@@ -6606,7 +6402,7 @@
         "tags": [
           "Coordinator"
         ],
-        "description": "Reads the in-memory child registry and dispatches ``close_workstream`` via the routing proxy for every direct child under a bounded (16-concurrency) semaphore.  Soft-close only \u2014 it does not touch grandchildren (the model-facing tool asks for a bounded teardown of its own direct fan-out).  Returns ``{closed, failed, skipped}`` where ``skipped`` distinguishes already-gone (404) from dispatch-broken (``failed``).  The optional ``reason`` propagates to every closed child's audit + workstream_config.  Writes ``coordinator.closed_all_children`` at the coord level.",
+        "description": "Reads the in-memory child registry and dispatches ``close_workstream`` via the routing proxy for every direct child under a bounded (16-concurrency) semaphore.  Unlike ``stop_cascade`` this does not touch grandchildren \u2014 the model-facing tool asks for a bounded teardown of its own fan-out.  Returns ``{closed, failed, skipped}`` where ``skipped`` distinguishes already-gone (404) from dispatch-broken (``failed``).  The optional ``reason`` propagates to every closed child's audit + workstream_config.  Writes ``coordinator.closed_all_children`` at the coord level.",
         "parameters": [
           {
             "name": "ws_id",
@@ -7785,12 +7581,6 @@
             "title": "Skill",
             "type": "string"
           },
-          "persona": {
-            "default": "",
-            "description": "Persona slug; resolved and snapshotted at creation, empty = kind default",
-            "title": "Persona",
-            "type": "string"
-          },
           "resume_ws": {
             "default": "",
             "description": "Workstream ID to resume (loads previous conversation)",
@@ -8118,12 +7908,6 @@
             "description": "Optional skill name to apply to the coordinator session.",
             "title": "Skill"
           },
-          "persona": {
-            "default": "",
-            "description": "Persona slug; resolved and snapshotted at creation, empty = kind default",
-            "title": "Persona",
-            "type": "string"
-          },
           "initial_message": {
             "default": "",
             "description": "Optional first user message dispatched to the new coordinator session.",
@@ -8288,7 +8072,7 @@
             "type": "string"
           },
           "attached_ids": {
-            "description": "Attachment ids actually attached to this turn. Subset of the request's `attachment_ids` (or the auto-consumed pending set). Empty when the send carries no attachments.",
+            "description": "Attachment ids actually reserved onto this turn. Subset of the request's `attachment_ids` (or the auto-consumed pending set). Empty when the send carries no attachments.",
             "items": {
               "type": "string"
             },
@@ -8336,6 +8120,42 @@
         "title": "CoordinatorSendResponse",
         "type": "object"
       },
+      "CoordinatorStopCascadeResponse": {
+        "description": "Response body for POST /v1/api/workstreams/{ws_id}/stop_cascade.",
+        "properties": {
+          "status": {
+            "default": "ok",
+            "title": "Status",
+            "type": "string"
+          },
+          "cancelled": {
+            "description": "Child ws_ids that accepted the cancel dispatch.",
+            "items": {
+              "type": "string"
+            },
+            "title": "Cancelled",
+            "type": "array"
+          },
+          "failed": {
+            "description": "Child ws_ids whose cancel dispatch returned an error other than an already-gone 404 \u2014 the cascade continues on per-child failure so a single unreachable node doesn't abort the whole batch.",
+            "items": {
+              "type": "string"
+            },
+            "title": "Failed",
+            "type": "array"
+          },
+          "skipped": {
+            "description": "Child ws_ids that returned 404 on cancel (already gone).  Reported separately from ``failed`` so operators can distinguish already-done from dispatch-broken.",
+            "items": {
+              "type": "string"
+            },
+            "title": "Skipped",
+            "type": "array"
+          }
+        },
+        "title": "CoordinatorStopCascadeResponse",
+        "type": "object"
+      },
       "CoordinatorTaskInfo": {
         "description": "Per-task row in the coordinator's task envelope.",
         "properties": {
@@ -11068,345 +10888,6 @@
         "title": "ListModelDefinitionsResponse",
         "type": "object"
       },
-      "PersonaInfo": {
-        "description": "Full persona row \u2014 the authoring shape (contrast PersonaChoice, the\npicker's display-only projection on the server surface).",
-        "properties": {
-          "persona_id": {
-            "title": "Persona Id",
-            "type": "string"
-          },
-          "name": {
-            "title": "Name",
-            "type": "string"
-          },
-          "display_name": {
-            "default": "",
-            "title": "Display Name",
-            "type": "string"
-          },
-          "description": {
-            "default": "",
-            "title": "Description",
-            "type": "string"
-          },
-          "base_prompt": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "description": "BASE-module override; null = the kind's stock base",
-            "title": "Base Prompt"
-          },
-          "tool_allowlist": {
-            "anyOf": [
-              {
-                "items": {
-                  "type": "string"
-                },
-                "type": "array"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "description": "Tool visibility set: null = unrestricted, [] = no tools, [names] = exact set (include 'tool_search' to keep the set soft/expandable)",
-            "title": "Tool Allowlist"
-          },
-          "mcp_enabled": {
-            "default": true,
-            "title": "Mcp Enabled",
-            "type": "boolean"
-          },
-          "memory_enabled": {
-            "default": true,
-            "title": "Memory Enabled",
-            "type": "boolean"
-          },
-          "applies_to_kinds": {
-            "items": {
-              "type": "string"
-            },
-            "title": "Applies To Kinds",
-            "type": "array"
-          },
-          "is_default": {
-            "default": false,
-            "title": "Is Default",
-            "type": "boolean"
-          },
-          "enabled": {
-            "default": true,
-            "description": "false = archived",
-            "title": "Enabled",
-            "type": "boolean"
-          },
-          "org_id": {
-            "default": "",
-            "title": "Org Id",
-            "type": "string"
-          },
-          "created_by": {
-            "default": "",
-            "title": "Created By",
-            "type": "string"
-          },
-          "created": {
-            "default": "",
-            "title": "Created",
-            "type": "string"
-          },
-          "updated": {
-            "default": "",
-            "title": "Updated",
-            "type": "string"
-          }
-        },
-        "required": [
-          "persona_id",
-          "name"
-        ],
-        "title": "PersonaInfo",
-        "type": "object"
-      },
-      "CreatePersonaRequest": {
-        "properties": {
-          "name": {
-            "description": "Immutable slug (lowercase: a-z, 0-9, '-', '_')",
-            "title": "Name",
-            "type": "string"
-          },
-          "display_name": {
-            "default": "",
-            "title": "Display Name",
-            "type": "string"
-          },
-          "description": {
-            "default": "",
-            "title": "Description",
-            "type": "string"
-          },
-          "base_prompt": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "description": "Inline BASE override \u2014 required. Every persona must name a prompt source; built-in file-backed personas are seeded by migration, not created here, so an operator-created persona must supply base_prompt.",
-            "title": "Base Prompt"
-          },
-          "tool_allowlist": {
-            "anyOf": [
-              {
-                "items": {
-                  "type": "string"
-                },
-                "type": "array"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Tool Allowlist"
-          },
-          "mcp_enabled": {
-            "default": true,
-            "title": "Mcp Enabled",
-            "type": "boolean"
-          },
-          "memory_enabled": {
-            "default": true,
-            "title": "Memory Enabled",
-            "type": "boolean"
-          },
-          "applies_to_kinds": {
-            "items": {
-              "type": "string"
-            },
-            "title": "Applies To Kinds",
-            "type": "array"
-          },
-          "is_default": {
-            "default": false,
-            "title": "Is Default",
-            "type": "boolean"
-          },
-          "enabled": {
-            "default": true,
-            "title": "Enabled",
-            "type": "boolean"
-          },
-          "org_id": {
-            "default": "",
-            "description": "Owning org (informational; capped at 64)",
-            "title": "Org Id",
-            "type": "string"
-          }
-        },
-        "required": [
-          "name"
-        ],
-        "title": "CreatePersonaRequest",
-        "type": "object"
-      },
-      "UpdatePersonaRequest": {
-        "description": "PATCH body \u2014 absent fields are left unchanged.\n\nExplicit ``null`` resets ``tool_allowlist`` to unrestricted, and \u2014 on a\nBUILT-IN persona only \u2014 clears ``base_prompt`` (the operator override),\nreverting to that persona's file-backed prompt.  An OPERATOR persona has no\nfallback source, so ``base_prompt: null`` on one is rejected: every persona\nmust name a prompt source.  ``null`` on the boolean flags or\n``applies_to_kinds`` is ignored (treated as absent), so a client serializing\nunset optionals as null cannot archive a persona or flip levers by accident.\n\nArchive = ``{\"enabled\": false}``; default flip = ``{\"is_default\": true}``\non the successor (storage demotes the incumbent atomically).  ``name``\nis immutable; existing workstreams are never affected by edits.",
-        "properties": {
-          "display_name": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Display Name"
-          },
-          "description": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Description"
-          },
-          "base_prompt": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Base Prompt"
-          },
-          "tool_allowlist": {
-            "anyOf": [
-              {
-                "items": {
-                  "type": "string"
-                },
-                "type": "array"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Tool Allowlist"
-          },
-          "mcp_enabled": {
-            "anyOf": [
-              {
-                "type": "boolean"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Mcp Enabled"
-          },
-          "memory_enabled": {
-            "anyOf": [
-              {
-                "type": "boolean"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Memory Enabled"
-          },
-          "applies_to_kinds": {
-            "anyOf": [
-              {
-                "items": {
-                  "type": "string"
-                },
-                "type": "array"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Applies To Kinds"
-          },
-          "is_default": {
-            "anyOf": [
-              {
-                "type": "boolean"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Is Default"
-          },
-          "enabled": {
-            "anyOf": [
-              {
-                "type": "boolean"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Enabled"
-          }
-        },
-        "title": "UpdatePersonaRequest",
-        "type": "object"
-      },
-      "ListPersonasResponse": {
-        "properties": {
-          "personas": {
-            "items": {
-              "$ref": "#/components/schemas/PersonaInfo"
-            },
-            "title": "Personas",
-            "type": "array"
-          },
-          "tool_inventory": {
-            "additionalProperties": {
-              "items": {
-                "type": "string"
-              },
-              "type": "array"
-            },
-            "description": "Per-kind builtin tool names (plus the synthetic 'tool_search') for the visibility checklist \u2014 derived server-side so clients never hand-mirror the inventory",
-            "title": "Tool Inventory",
-            "type": "object"
-          }
-        },
-        "required": [
-          "personas"
-        ],
-        "title": "ListPersonasResponse",
-        "type": "object"
-      },
       "ModelReloadResponse": {
         "properties": {
           "status": {
@@ -11449,11 +10930,6 @@
             "default": "",
             "title": "Definition Id",
             "type": "string"
-          },
-          "supports_rerank": {
-            "default": false,
-            "title": "Supports Rerank",
-            "type": "boolean"
           }
         },
         "title": "DetectModelRequest",
@@ -11520,80 +10996,11 @@
             ],
             "default": null,
             "title": "Error"
-          },
-          "capabilities": {
-            "additionalProperties": true,
-            "title": "Capabilities",
-            "type": "object"
-          },
-          "rerank_calibration_note": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Rerank Calibration Note"
           }
         },
         "title": "DetectModelResponse",
         "type": "object"
       },
-      "CalibrateModelResponse": {
-        "properties": {
-          "separated": {
-            "default": false,
-            "title": "Separated",
-            "type": "boolean"
-          },
-          "suggested_threshold": {
-            "anyOf": [
-              {
-                "type": "number"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Suggested Threshold"
-          },
-          "raw_scale": {
-            "default": "",
-            "title": "Raw Scale",
-            "type": "string"
-          },
-          "relevant": {
-            "items": {
-              "type": "number"
-            },
-            "title": "Relevant",
-            "type": "array"
-          },
-          "irrelevant": {
-            "items": {
-              "type": "number"
-            },
-            "title": "Irrelevant",
-            "type": "array"
-          },
-          "applied": {
-            "default": false,
-            "title": "Applied",
-            "type": "boolean"
-          },
-          "error": {
-            "default": "",
-            "title": "Error",
-            "type": "string"
-          }
-        },
-        "title": "CalibrateModelResponse",
-        "type": "object"
-      },
       "ModelCapabilitiesResponse": {
         "properties": {
           "model": {
@@ -13306,17 +12713,21 @@
           },
           "pending_approval": {
             "default": false,
-            "description": "True when at least one approval cycle is live (a gate thread parked awaiting an operator approve/deny).  Mirrors the same field on ``DashboardWorkstream`` / cluster live projections so a freshly-loaded chat tab can render the inline approval gate from the detail snapshot before SSE replay arrives.",
+            "description": "True when the workstream is parked on ``_approval_event`` awaiting an operator approve/deny.  Mirrors the same field on ``DashboardWorkstream`` / cluster live projections so a freshly-loaded chat tab can render the inline approval gate from the detail snapshot before SSE replay arrives.",
             "title": "Pending Approval",
             "type": "boolean"
           },
-          "pending_approval_details": {
-            "description": "Inline approval payloads, one per live cycle, oldest first \u2014 same shape as ``DashboardWorkstream.pending_approval_details``.  Empty when no approval is pending.  Lets a reload paint every action row + judge verdicts immediately instead of relying on the SSE approve_request replay timing window.  Replaces 1.6's ``pending_approval_detail`` single-object field (breaking, 1.7).",
-            "items": {
-              "$ref": "#/components/schemas/PendingApprovalDetail"
-            },
-            "title": "Pending Approval Details",
-            "type": "array"
+          "pending_approval_detail": {
+            "anyOf": [
+              {
+                "$ref": "#/components/schemas/PendingApprovalDetail"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "description": "Inline approval payload \u2014 same shape as ``DashboardWorkstream.pending_approval_detail``.  ``None`` when no approval is pending.  Lets a reload paint the action row + judge verdicts immediately instead of relying on the SSE approve_request replay timing window."
           }
         },
         "required": [
@@ -13329,14 +12740,8 @@
         "type": "object"
       },
       "PendingApprovalDetail": {
-        "description": "Inline approval payload merged into ``DashboardWorkstream``.\n\nOne entry per live approval CYCLE \u2014 a gate thread parked in\n``approve_tools`` awaiting the operator.  Parallel task agents run\nconcurrent gates, so a workstream can have several of these at\nonce (``pending_approval_details``, oldest first).  Cross-tenant\nexposure here follows the same trusted-team posture as\n``activity`` / ``tokens`` \u2014 see ``server.py``'s ``dashboard``\nhandler comment.",
+        "description": "Inline approval payload merged into ``DashboardWorkstream``.\n\nSet when a workstream's ``approve_tools`` is parked on\n``_approval_event``; ``None`` (omitted) otherwise. Cross-tenant\nexposure here follows the same trusted-team posture as\n``activity`` / ``tokens`` \u2014 see ``server.py``'s ``dashboard``\nhandler comment.",
         "properties": {
-          "cycle_id": {
-            "default": "",
-            "description": "Identity of this approval cycle.  Echo it back on ``POST /v1/api/workstreams/{ws_id}/approve`` to resolve exactly this round \u2014 required for correctness when several cycles are live (parallel task agents).",
-            "title": "Cycle Id",
-            "type": "string"
-          },
           "call_id": {
             "default": "",
             "description": "Primary call_id \u2014 first non-empty call_id in items list order. Matches the 409 ``current_call_id`` response from ``POST /v1/api/workstreams/{ws_id}/approve`` so the UI can render the same identifier the server reports as current.",
@@ -13452,26 +12857,13 @@
             "type": "string"
           },
           "messages": {
-            "description": "Tail of the workstream's message history, projected to the canonical render shape (``role`` may be ``system`` for operator-context turns; flat tool_calls with verdict / output_assessment; top-level source / attachments / reasoning; derived denied / is_error / pending). Bounded by the ``limit`` query parameter (default 100, max 500).",
+            "description": "Tail of the workstream's message history, projected to the canonical render shape (flat tool_calls with verdict / output_assessment, top-level source / reminders / attachments, derived denied / is_error / pending). Bounded by the ``limit`` query parameter (default 100, max 500).",
             "items": {
               "additionalProperties": true,
               "type": "object"
             },
             "title": "Messages",
             "type": "array"
-          },
-          "cursor": {
-            "anyOf": [
-              {
-                "type": "integer"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "description": "SSE resume cursor (a ``Last-Event-ID`` value). Non-null only when the trailing turn is an executing in-flight tool batch that the live ring buffer can replay: ``messages`` then omits that turn and the client opens its initial SSE with this cursor so the existing delta replay fast-forwards the in-flight turn (tool calls, results, prompts) instead of the lossy synthetic snapshot. Null on every other read \u2014 the client connects fresh.",
-            "title": "Cursor"
           }
         },
         "required": [

sdk/typescript/package-lock.json

@@ -14,21 +14,21 @@
       }
     },
     "node_modules/@emnapi/core": {
-      "version": "1.11.1",
-      "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.11.1.tgz",
-      "integrity": "sha512-RSvbQmHzdKzNsLYa/wHrbc3KN4sYLKAdPZxqiM2HATqv/SBk2/ENSHpvXGaLOMcsAyz0poEGqkmmKYG3OWiJEQ==",
+      "version": "1.10.0",
+      "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.10.0.tgz",
+      "integrity": "sha512-yq6OkJ4p82CAfPl0u9mQebQHKPJkY7WrIuk205cTYnYe+k2Z8YBh11FrbRG/H6ihirqcacOgl2BIO8oyMQLeXw==",
       "dev": true,
       "license": "MIT",
       "optional": true,
       "dependencies": {
-        "@emnapi/wasi-threads": "1.2.2",
+        "@emnapi/wasi-threads": "1.2.1",
         "tslib": "^2.4.0"
       }
     },
     "node_modules/@emnapi/runtime": {
-      "version": "1.11.1",
-      "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.1.tgz",
-      "integrity": "sha512-vgj7R3y3Wgx24IQaGPA/R6YFXLHVMOZ0uVEyIQPaWs+rd1AzfEMXlAC22FYwO1XkKR6NPsq7mUandH8oIRdZFw==",
+      "version": "1.10.0",
+      "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.10.0.tgz",
+      "integrity": "sha512-ewvYlk86xUoGI0zQRNq/mC+16R1QeDlKQy21Ki3oSYXNgLb45GV1P6A0M+/s6nyCuNDqe5VpaY84BzXGwVbwFA==",
       "dev": true,
       "license": "MIT",
       "optional": true,
@@ -37,9 +37,9 @@
       }
     },
     "node_modules/@emnapi/wasi-threads": {
-      "version": "1.2.2",
-      "resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.2.2.tgz",
-      "integrity": "sha512-c95qOXkHdydNKhscBTebqEC1CVAZpyqOfVfBzQ1qgzyl3gfeldUjIggDbIZgDKsHLgnsM+igH7TJ/eAasaVuMA==",
+      "version": "1.2.1",
+      "resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.2.1.tgz",
+      "integrity": "sha512-uTII7OYF+/Mes/MrcIOYp5yOtSMLBWSIoLPpcgwipoiKbli6k322tcoFsxoIIxPDqW01SQGAgko4EzZi2BNv2w==",
       "dev": true,
       "license": "MIT",
       "optional": true,
@@ -55,14 +55,14 @@
       "license": "MIT"
     },
     "node_modules/@napi-rs/wasm-runtime": {
-      "version": "1.1.6",
-      "resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.1.6.tgz",
-      "integrity": "sha512-ZLv/JdUfkvOy9eCnnBaGfiO+XimbjebAeO+MRQqD/B+FR1tnRN0tpKSJHRbE8sFfS6aqsXZ67TQjfwfsxULVbg==",
+      "version": "1.1.4",
+      "resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.1.4.tgz",
+      "integrity": "sha512-3NQNNgA1YSlJb/kMH1ildASP9HW7/7kYnRI2szWJaofaS1hWmbGI4H+d3+22aGzXXN9IJ+n+GiFVcGipJP18ow==",
       "dev": true,
       "license": "MIT",
       "optional": true,
       "dependencies": {
-        "@tybys/wasm-util": "^0.10.3"
+        "@tybys/wasm-util": "^0.10.1"
       },
       "funding": {
         "type": "github",
@@ -74,9 +74,9 @@
       }
     },
     "node_modules/@oxc-project/types": {
-      "version": "0.138.0",
-      "resolved": "https://registry.npmjs.org/@oxc-project/types/-/types-0.138.0.tgz",
-      "integrity": "sha512-1a7ZKmrRTCoN1XMZ4L0PyyqrMnrNlLyPuOkdSX2MZg7IiIGRUyurNhAm73ptDOraoBcIordsIGKNPKUzy3ZmfA==",
+      "version": "0.133.0",
+      "resolved": "https://registry.npmjs.org/@oxc-project/types/-/types-0.133.0.tgz",
+      "integrity": "sha512-KzkdCd6Uxqnf6l3HOw1xfatAlUURA0g14cvBYFyJ5SaNOQbOUvBr9PKArcPcrNIeRsBdgcUzOGrhKveVpvOIGA==",
       "dev": true,
       "license": "MIT",
       "funding": {
@@ -84,9 +84,9 @@
       }
     },
     "node_modules/@rolldown/binding-android-arm64": {
-      "version": "1.1.4",
-      "resolved": "https://registry.npmjs.org/@rolldown/binding-android-arm64/-/binding-android-arm64-1.1.4.tgz",
-      "integrity": "sha512-EZLpf/8y7GXkkra90ML47kzik/GMP3EMcE9bPyHmRfxLC6z9+aW5A8poCsoxjrT5GfEcNAAvWwUHjvP1pUQkfw==",
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/@rolldown/binding-android-arm64/-/binding-android-arm64-1.0.3.tgz",
+      "integrity": "sha512-454rs7jHngixp/NMxd5srYD57OnzSlZ/eFTETjORQHLwJG1lRtmNOJcBerZlfu4GjKqeq8aCCIQrMdHyhI51Hw==",
       "cpu": [
         "arm64"
       ],
@@ -101,9 +101,9 @@
       }
     },
     "node_modules/@rolldown/binding-darwin-arm64": {
-      "version": "1.1.4",
-      "resolved": "https://registry.npmjs.org/@rolldown/binding-darwin-arm64/-/binding-darwin-arm64-1.1.4.tgz",
-      "integrity": "sha512-aUi+HBvmYb7j8krl1+qJgkG8C17fO79gk3c+jPw4S8glRFc1DTija9S3EyaTSQUm5GJXYKDAsugBEhFHH2vYiQ==",
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/@rolldown/binding-darwin-arm64/-/binding-darwin-arm64-1.0.3.tgz",
+      "integrity": "sha512-PcAhP+ynjURNyy8SKGl5DQP94aGuB/7JrXJb/t7P+hanXvQVMWzUvRRhBAcg/lNRadBhoUPqSoP4xw5tR/KBEA==",
       "cpu": [
         "arm64"
       ],
@@ -118,9 +118,9 @@
       }
     },
     "node_modules/@rolldown/binding-darwin-x64": {
-      "version": "1.1.4",
-      "resolved": "https://registry.npmjs.org/@rolldown/binding-darwin-x64/-/binding-darwin-x64-1.1.4.tgz",
-      "integrity": "sha512-F7hHC3gwY11+vByKPRWqwGbeXWVgKmL+pTGCinaEhdihzBV2aQ0fvZOch9cXYUOKuKKq429HeYXOqQLc7wFCEg==",
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/@rolldown/binding-darwin-x64/-/binding-darwin-x64-1.0.3.tgz",
+      "integrity": "sha512-9YpfeUvSE2RS7wysJ81uOZkXJz7f7Q55H2Gvp3VEw/EsahqDtrphrZ0EwDLK5vvKOzaCrBsjF8JmnMLcUt78Gg==",
       "cpu": [
         "x64"
       ],
@@ -135,9 +135,9 @@
       }
     },
     "node_modules/@rolldown/binding-freebsd-x64": {
-      "version": "1.1.4",
-      "resolved": "https://registry.npmjs.org/@rolldown/binding-freebsd-x64/-/binding-freebsd-x64-1.1.4.tgz",
-      "integrity": "sha512-sI5yw+7s92SK6odiEhD5lKCBlWcpjHS5qyqpVQbZAJ0fIzEUXrmbl3DH2ybR3PZogulNJF+COLtmA8hUfvkCCQ==",
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/@rolldown/binding-freebsd-x64/-/binding-freebsd-x64-1.0.3.tgz",
+      "integrity": "sha512-yB1IlAsSNHncV6SCTL27/MVGR5htvQsoGxIv5KMGXALp+Ll1wYsn+x98M9MW7qa+NdSbvrrY7ANI4wLJ0n1e6g==",
       "cpu": [
         "x64"
       ],
@@ -152,9 +152,9 @@
       }
     },
     "node_modules/@rolldown/binding-linux-arm-gnueabihf": {
-      "version": "1.1.4",
-      "resolved": "https://registry.npmjs.org/@rolldown/binding-linux-arm-gnueabihf/-/binding-linux-arm-gnueabihf-1.1.4.tgz",
-      "integrity": "sha512-mCi0OKgEieFircrtVYmQAFGszRtMnZ6fpZAXrxanXAu7lqZcsK1E1RAaZNG0uKAnxox3B1f4EyQNnoyMfN1vAA==",
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/@rolldown/binding-linux-arm-gnueabihf/-/binding-linux-arm-gnueabihf-1.0.3.tgz",
+      "integrity": "sha512-Yi30IVAAfLUCy2MseFjbB1jAMDl1VMCAas5StnYp8da9+CKvMd2H2cbEjWcw5NPaPqzvYkVIaF1nNUG+b7u/sw==",
       "cpu": [
         "arm"
       ],
@@ -169,9 +169,9 @@
       }
     },
     "node_modules/@rolldown/binding-linux-arm64-gnu": {
-      "version": "1.1.4",
-      "resolved": "https://registry.npmjs.org/@rolldown/binding-linux-arm64-gnu/-/binding-linux-arm64-gnu-1.1.4.tgz",
-      "integrity": "sha512-B9Ial3Kv5sh0SHnB1g/QWcUQCEvCF6QKGAl4zXypYj65mVI+B4AhFBwPtSN7pDrJeIx8Z7zdy4ntx+wQABom7w==",
+      "version": "1.0.3",
+      "resolved": "https://registry.npmjs.org/@rolldown/binding-linux-arm64-gnu/-/binding-linux-arm64-gnu-1.0.3.tgz",
+      "integrity": "sha512-jsO7R8To+AdlYgUmN5sHSCZbfhtMBkO0WUx8iORQnPcMMdgr7qM2DQmMwgabs3GhNztdmoKkMKQFHD6DTMCIQw==",
       "cpu": [
         "arm64"
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@@ -189,9 +189,9 @@
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-        "@rolldown/binding-android-arm64": "1.1.4",
-        "@rolldown/binding-darwin-arm64": "1.1.4",
-        "@rolldown/binding-darwin-x64": "1.1.4",
-        "@rolldown/binding-freebsd-x64": "1.1.4",
-        "@rolldown/binding-linux-arm-gnueabihf": "1.1.4",
-        "@rolldown/binding-linux-arm64-gnu": "1.1.4",
-        "@rolldown/binding-linux-arm64-musl": "1.1.4",
-        "@rolldown/binding-linux-ppc64-gnu": "1.1.4",
-        "@rolldown/binding-linux-s390x-gnu": "1.1.4",
-        "@rolldown/binding-linux-x64-gnu": "1.1.4",
-        "@rolldown/binding-linux-x64-musl": "1.1.4",
-        "@rolldown/binding-openharmony-arm64": "1.1.4",
-        "@rolldown/binding-wasm32-wasi": "1.1.4",
-        "@rolldown/binding-win32-arm64-msvc": "1.1.4",
-        "@rolldown/binding-win32-x64-msvc": "1.1.4"
+        "@rolldown/binding-android-arm64": "1.0.3",
+        "@rolldown/binding-darwin-arm64": "1.0.3",
+        "@rolldown/binding-darwin-x64": "1.0.3",
+        "@rolldown/binding-freebsd-x64": "1.0.3",
+        "@rolldown/binding-linux-arm-gnueabihf": "1.0.3",
+        "@rolldown/binding-linux-arm64-gnu": "1.0.3",
+        "@rolldown/binding-linux-arm64-musl": "1.0.3",
+        "@rolldown/binding-linux-ppc64-gnu": "1.0.3",
+        "@rolldown/binding-linux-s390x-gnu": "1.0.3",
+        "@rolldown/binding-linux-x64-gnu": "1.0.3",
+        "@rolldown/binding-linux-x64-musl": "1.0.3",
+        "@rolldown/binding-openharmony-arm64": "1.0.3",
+        "@rolldown/binding-wasm32-wasi": "1.0.3",
+        "@rolldown/binding-win32-arm64-msvc": "1.0.3",
+        "@rolldown/binding-win32-x64-msvc": "1.0.3"
       }
     },
     "node_modules/siginfo": {
@@ -1122,16 +1122,16 @@
       }
     },
     "node_modules/vite": {
-      "version": "8.1.2",
-      "resolved": "https://registry.npmjs.org/vite/-/vite-8.1.2.tgz",
-      "integrity": "sha512-6YYPbRXTxx6bRXmOn7XdnQAy5DQNHhDgtjhDHI13oe4pY93kkcdGJWxpGwOm++/Wh0QpQhDrpIoVMrmrsI5AGQ==",
+      "version": "8.0.16",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-8.0.16.tgz",
+      "integrity": "sha512-h9bXPmJichP5fLmVQo3PyaGSDE2n3aPuomeAlVRm0JLmt4rY6zmPKd59HYI4LNW8oTK7tlTsuC7l/m7awx9Jcw==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
         "lightningcss": "^1.32.0",
         "picomatch": "^4.0.4",
-        "postcss": "^8.5.16",
-        "rolldown": "~1.1.3",
+        "postcss": "^8.5.15",
+        "rolldown": "1.0.3",
         "tinyglobby": "^0.2.17"
       },
       "bin": {
@@ -1148,7 +1148,7 @@
       },
       "peerDependencies": {
         "@types/node": "^20.19.0 || >=22.12.0",
-        "@vitejs/devtools": "^0.3.0",
+        "@vitejs/devtools": "^0.1.18",
         "esbuild": "^0.27.0 || ^0.28.0",
         "jiti": ">=1.21.0",
         "less": "^4.0.0",
@@ -1200,19 +1200,19 @@
       }
     },
     "node_modules/vitest": {
-      "version": "4.1.9",
-      "resolved": "https://registry.npmjs.org/vitest/-/vitest-4.1.9.tgz",
-      "integrity": "sha512-nE3/LEyc0z87uHYLZebqCUOaJr2hdtuPp7BQ4BosVFnfltxgAvMG08NyrSGlPpOUWvR27c5flSmYFTNr78L9GQ==",
+      "version": "4.1.8",
+      "resolved": "https://registry.npmjs.org/vitest/-/vitest-4.1.8.tgz",
+      "integrity": "sha512-flY6ScbCIt9HThs+C5HS7jvGOB560DJtk/Z15IQROTA6zEy49Nh8T/dofWTQL+n3vswqn87sbJNiuqw1SDp5Ig==",
       "dev": true,
       "license": "MIT",
       "dependencies": {
-        "@vitest/expect": "4.1.9",
-        "@vitest/mocker": "4.1.9",
-        "@vitest/pretty-format": "4.1.9",
-        "@vitest/runner": "4.1.9",
-        "@vitest/snapshot": "4.1.9",
-        "@vitest/spy": "4.1.9",
-        "@vitest/utils": "4.1.9",
+        "@vitest/expect": "4.1.8",
+        "@vitest/mocker": "4.1.8",
+        "@vitest/pretty-format": "4.1.8",
+        "@vitest/runner": "4.1.8",
+        "@vitest/snapshot": "4.1.8",
+        "@vitest/spy": "4.1.8",
+        "@vitest/utils": "4.1.8",
         "es-module-lexer": "^2.0.0",
         "expect-type": "^1.3.0",
         "magic-string": "^0.30.21",
@@ -1240,12 +1240,12 @@
         "@edge-runtime/vm": "*",
         "@opentelemetry/api": "^1.9.0",
         "@types/node": "^20.0.0 || ^22.0.0 || >=24.0.0",
-        "@vitest/browser-playwright": "4.1.9",
-        "@vitest/browser-preview": "4.1.9",
-        "@vitest/browser-webdriverio": "4.1.9",
-        "@vitest/coverage-istanbul": "4.1.9",
-        "@vitest/coverage-v8": "4.1.9",
-        "@vitest/ui": "4.1.9",
+        "@vitest/browser-playwright": "4.1.8",
+        "@vitest/browser-preview": "4.1.8",
+        "@vitest/browser-webdriverio": "4.1.8",
+        "@vitest/coverage-istanbul": "4.1.8",
+        "@vitest/coverage-v8": "4.1.8",
+        "@vitest/ui": "4.1.8",
         "happy-dom": "*",
         "jsdom": "*",
         "vite": "^6.0.0 || ^7.0.0 || ^8.0.0"

sdk/typescript/src/events.ts

@@ -75,35 +75,15 @@ export interface ToolInfoEvent {
   items: Array<Record<string, unknown>>;
 }
 
-/** One approval CYCLE awaiting the operator.  Several can be outstanding
- *  at once (parallel task agents each gate their own tool calls) — key
- *  prompt UI by `cycle_id` and echo it back on the approve POST.
- *
- *  `cycle_id` is optional because it was added in 1.7: a pre-1.7 server
- *  omits it on the wire, so a current SDK talking to an older node sees
- *  `undefined`.  Resolve those the legacy way (no selector → oldest
- *  cycle). A current server always sends it. */
 export interface ApproveRequestEvent {
   type: "approve_request";
-  cycle_id?: string;
   items: Array<Record<string, unknown>>;
-  judge_pending?: boolean;
 }
 
-/** A specific approval cycle resolved; `cycle_id`/`call_ids` identify
- *  which prompt to dismiss.
- *
- *  Both are optional for the same reason as `ApproveRequestEvent.cycle_id`
- *  — a pre-1.7 server emits neither, so a bare "something resolved"
- *  dismisses the sole tracked prompt (the legacy fallback the UI and
- *  channel adapters keep). A current server always sends both. */
 export interface ApprovalResolvedEvent {
   type: "approval_resolved";
   approved: boolean;
   feedback: string;
-  always?: boolean;
-  cycle_id?: string;
-  call_ids?: string[];
 }
 
 export interface ToolResultEvent {

sdk/typescript/src/server.ts

@@ -166,13 +166,6 @@ export class TurnstoneServer extends BaseClient {
     approved?: boolean;
     feedback?: string | null;
     always?: boolean;
-    /** Resolve exactly this approval cycle (from ApproveRequestEvent.cycle_id).
-     *  Omitting it resolves the OLDEST live cycle — ambiguous when parallel
-     *  task agents have several prompts outstanding, so pass it whenever the
-     *  triggering event is known. */
-    cycleId?: string;
-    /** Alternative selector: any call_id inside the target cycle. */
-    callId?: string;
   }): Promise<StatusResponse> {
     return this.request(
       "POST",
@@ -182,8 +175,6 @@ export class TurnstoneServer extends BaseClient {
           approved: opts.approved ?? true,
           feedback: opts.feedback,
           always: opts.always,
-          cycle_id: opts.cycleId,
-          call_id: opts.callId,
         },
       },
     );

sdk/typescript/src/types.ts

@@ -130,17 +130,6 @@ export interface CreateWorkstreamRequest {
   auto_approve?: boolean;
   resume_ws?: string;
   skill?: string;
-  /**
-   * Persona name (slug) to create the workstream with. Resolved and
-   * snapshotted at creation — later persona edits never affect this
-   * workstream. Empty selects the kind's default persona.
-   */
-  persona?: string;
-  /**
-   * Optional project to attach this workstream to. Drives the shared
-   * `project` memory scope; coordinator children inherit the parent's project.
-   */
-  project_id?: string;
   /** First user message dispatched in a background worker after creation. */
   initial_message?: string;
   /**
@@ -185,7 +174,6 @@ export interface WorkstreamInfo {
   kind: string;
   parent_ws_id: string | null;
   user_id: string;
-  project_id: string | null;
 }
 
 export interface ListWorkstreamsResponse {
@@ -215,7 +203,6 @@ export interface DashboardWorkstream {
   ws_id: string;
   name: string;
   state: string;
-  project_id: string | null;
   title?: string;
   tokens?: number;
   context_ratio?: number;
@@ -262,8 +249,6 @@ export interface SavedWorkstreamInfo {
   child_count?: number;
   context_tokens?: number;
   context_ratio?: number;
-  /** Persona slug the workstream was created with (empty/absent = pre-persona). */
-  persona?: string | null;
 }
 
 export interface ListSavedWorkstreamsResponse {
@@ -532,8 +517,6 @@ export interface ConsoleCreateWsRequest {
   model?: string;
   initial_message?: string;
   skill?: string;
-  /** Persona slug — resolved and snapshotted at creation. */
-  persona?: string;
   resume_ws?: string;
 }
 
@@ -799,7 +782,7 @@ export interface SaveMemoryRequest {
   name: string;
   content: string;
   description?: string;
-  type?: "user" | "general" | "feedback" | "reference";
+  type?: "user" | "project" | "feedback" | "reference";
   scope?: "global" | "workstream" | "user";
   scope_id?: string;
 }

sdk/typescript/tests/server-attachments.test.ts

@@ -104,6 +104,7 @@ describe("TurnstoneServer attachments", () => {
     const [, init] = (fetchFn as ReturnType<typeof vi.fn>).mock.calls[0];
     expect(JSON.parse(init.body)).toEqual({
       message: "hi",
+      ws_id: "ws-X",
       attachment_ids: ["a1", "a2"],
     });
   });
@@ -116,7 +117,7 @@ describe("TurnstoneServer attachments", () => {
     });
     await client.send("hi", "ws-X");
     const [, init] = (fetchFn as ReturnType<typeof vi.fn>).mock.calls[0];
-    expect(JSON.parse(init.body)).toEqual({ message: "hi" });
+    expect(JSON.parse(init.body)).toEqual({ message: "hi", ws_id: "ws-X" });
   });
 
   it("createWorkstream with attachments sends multipart and auto-generates ws_id", async () => {

sdk/typescript/tests/server.test.ts

@@ -74,8 +74,8 @@ describe("TurnstoneServer", () => {
     await client.send("Hello", "ws1");
 
     const [url, init] = (fetchFn as ReturnType<typeof vi.fn>).mock.calls[0];
-    expect(url).toBe("http://test/v1/api/workstreams/ws1/send");
-    expect(JSON.parse(init.body)).toEqual({ message: "Hello" });
+    expect(url).toBe("http://test/v1/api/send");
+    expect(JSON.parse(init.body)).toEqual({ message: "Hello", ws_id: "ws1" });
   });
 
   it("injects auth header when token provided", async () => {

tests/_replay_helpers.py

@@ -51,12 +51,6 @@ def make_replay_mocks(
     ui._ws_messages = 0
     for key, value in ui_overrides.items():
         setattr(ui, key, value)
-    # Both replay paths read cycle cards via ``pending_approval_cards()``
-    # (one card per concurrent approval cycle).  Model it from the
-    # single-slot ``_pending_approval`` override so tests keep seeding
-    # the one field; a bare MagicMock here would iterate empty and
-    # silently drop the approve_request from the replay.
-    ui.pending_approval_cards = lambda: [ui._pending_approval] if ui._pending_approval else []
     ws = MagicMock()
     ws.session = session
     request = MagicMock()

tests/_wire_capture.py

@@ -1,76 +0,0 @@
-"""Recording fake SDK client — captures the kwargs at each provider's seam.
-
-Every provider's ``create_streaming`` assembles its kwargs and calls the
-SDK *eagerly* before returning the stream iterator (Anthropic
-``client.messages.stream``, OpenAI ``client.chat.completions.create``,
-Responses ``client.responses.create/stream``), so driving a provider
-against a :class:`RecordingClient` captures the full composed request
-payload without a network round-trip.
-
-Shared by the wire-payload golden harness (``test_wire_payload_golden``)
-and the effort-ladder parity harness (``test_effort_ladder_wire_parity``)
-so both assert against the same capture seam.
-"""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any
-
-if TYPE_CHECKING:
-    from collections.abc import Iterator
-
-
-class _EmptyStream:
-    """Stand-in for an SDK stream / stream-manager: empty iterable AND no-op CM."""
-
-    def __iter__(self) -> Iterator[Any]:
-        return iter(())
-
-    def __enter__(self) -> _EmptyStream:
-        return self
-
-    def __exit__(self, *exc: object) -> None:
-        return None
-
-
-class _Seam:
-    """Records the kwargs of a single SDK call, returns an empty stream stub."""
-
-    def __init__(self, sink: dict[str, Any]) -> None:
-        self._sink = sink
-
-    def __call__(self, **kwargs: Any) -> _EmptyStream:
-        # Last write wins; only one seam is exercised per provider call.
-        self._sink["payload"] = kwargs
-        return _EmptyStream()
-
-
-class _Completions:
-    def __init__(self, sink: dict[str, Any]) -> None:
-        self.create = _Seam(sink)
-
-
-class _Chat:
-    def __init__(self, sink: dict[str, Any]) -> None:
-        self.completions = _Completions(sink)
-
-
-class _Messages:
-    def __init__(self, sink: dict[str, Any]) -> None:
-        self.stream = _Seam(sink)
-
-
-class _Responses:
-    def __init__(self, sink: dict[str, Any]) -> None:
-        self.create = _Seam(sink)
-        self.stream = _Seam(sink)
-
-
-class RecordingClient:
-    """Fake SDK client exposing every provider's call seam, recording kwargs."""
-
-    def __init__(self) -> None:
-        self.captured: dict[str, Any] = {}
-        self.messages = _Messages(self.captured)
-        self.chat = _Chat(self.captured)
-        self.responses = _Responses(self.captured)

tests/conftest.py

@@ -52,76 +52,8 @@ def serve_until_exit(server: Any) -> None:
         loop.close()
 
 
-class _PendingResolver:
-    """Race-free drop-in for ``threading.Timer(delay, ui.resolve_approval)``.
-
-    ``approve_tools`` runs ``_approval_event.clear()`` -> register
-    ``_pending_approval`` -> ``_approval_event.wait(_APPROVAL_WAIT_TIMEOUT)``
-    (3600s). A *fixed-delay* timer can fire ``resolve_approval``
-    (``_approval_event.set()``) BEFORE that ``.clear()`` on a slow/loaded
-    runner, so the set is wiped by the clear and ``approve_tools`` blocks the
-    full hour -- surfacing as a CI hang. This instead waits until the approval
-    is actually registered (which happens *after* the clear), then resolves, so
-    the wakeup can never be lost. ``start()`` / ``cancel()`` mirror
-    ``threading.Timer`` so it drops into existing scaffolding. ``cancel()``
-    signals the worker to stop and joins it, so a test that errors *before* the
-    approval registers can't leak the thread or resolve late into a finished
-    test. ``before`` runs just before resolving -- e.g. to snapshot
-    pending-state fields the test asserts on.
-    """
-
-    def __init__(
-        self,
-        ui: Any,
-        *args: Any,
-        before: Callable[[], None] | None = None,
-        deadline: float = 10.0,
-        **kwargs: Any,
-    ) -> None:
-        self._ui = ui
-        self._args = args
-        self._kwargs = kwargs
-        self._before = before
-        self._deadline = deadline
-        self._cancelled = threading.Event()
-        self._started = False
-        self._thread = threading.Thread(target=self._run, name="resolve-when-pending", daemon=True)
-
-    def _run(self) -> None:
-        end = time.monotonic() + self._deadline
-        while time.monotonic() < end:
-            if self._cancelled.is_set():
-                return
-            # getattr (not a bare read) so a UI without _pending_approval can't
-            # crash the worker into a silent death that leaves approve_tools
-            # blocked for the full _APPROVAL_WAIT_TIMEOUT.
-            if getattr(self._ui, "_pending_approval", None) is not None:
-                if self._before is not None:
-                    self._before()
-                self._ui.resolve_approval(*self._args, **self._kwargs)
-                return
-            time.sleep(0.001)
-        # Deadline without registration: approve_tools isn't parked on the
-        # approval event (returned early, or never reached it) -- don't resolve
-        # into an unknown state; let the test's own assertions speak.
-
-    def start(self) -> None:
-        self._started = True
-        self._thread.start()
-
-    def cancel(self) -> None:
-        self._cancelled.set()
-        if self._started:
-            self._thread.join(timeout=5)
-
-
-def resolve_when_pending(ui: Any, *args: Any, **kwargs: Any) -> _PendingResolver:
-    """Build a race-free approval resolver (see :class:`_PendingResolver`)."""
-    return _PendingResolver(ui, *args, **kwargs)
-
-
 if TYPE_CHECKING:
-    from collections.abc import Callable, Iterator
+    from collections.abc import Iterator
 
     from turnstone.core.mcp_client import MCPClientManager, StaticServerState
     from turnstone.core.mcp_crypto import MCPTokenCipher

tests/data/wire_payloads/anthropic_compat__mid_orphan.json

@@ -1,78 +0,0 @@
-{
-  "cache_control": {
-    "type": "ephemeral"
-  },
-  "extra_body": {
-    "chat_template_kwargs": {
-      "enable_thinking": true,
-      "reasoning_effort": "high"
-    }
-  },
-  "max_tokens": 4096,
-  "messages": [
-    {
-      "content": "Weather in Paris and London?",
-      "role": "user"
-    },
-    {
-      "content": [
-        {
-          "id": "call_1",
-          "input": {
-            "city": "Paris"
-          },
-          "name": "get_weather",
-          "type": "tool_use"
-        },
-        {
-          "id": "call_2",
-          "input": {
-            "city": "London"
-          },
-          "name": "get_weather",
-          "type": "tool_use"
-        }
-      ],
-      "role": "assistant"
-    },
-    {
-      "content": [
-        {
-          "content": "18C, clear.",
-          "tool_use_id": "call_1",
-          "type": "tool_result"
-        },
-        {
-          "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
-          "is_error": true,
-          "tool_use_id": "call_2",
-          "type": "tool_result"
-        },
-        {
-          "text": "Actually, never mind London.",
-          "type": "text"
-        }
-      ],
-      "role": "user"
-    }
-  ],
-  "model": "qwen3.6-27b",
-  "temperature": 0.5,
-  "tools": [
-    {
-      "description": "Look up the weather for a city.",
-      "input_schema": {
-        "properties": {
-          "city": {
-            "type": "string"
-          }
-        },
-        "required": [
-          "city"
-        ],
-        "type": "object"
-      },
-      "name": "get_weather"
-    }
-  ]
-}

tests/data/wire_payloads/anthropic_compat__multipart.json

@@ -1,33 +0,0 @@
-{
-  "cache_control": {
-    "type": "ephemeral"
-  },
-  "extra_body": {
-    "chat_template_kwargs": {
-      "enable_thinking": true,
-      "reasoning_effort": "high"
-    }
-  },
-  "max_tokens": 4096,
-  "messages": [
-    {
-      "content": [
-        {
-          "text": "What's in this image?",
-          "type": "text"
-        },
-        {
-          "source": {
-            "data": "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==",
-            "media_type": "image/png",
-            "type": "base64"
-          },
-          "type": "image"
-        }
-      ],
-      "role": "user"
-    }
-  ],
-  "model": "qwen3.6-27b",
-  "temperature": 0.5
-}

tests/data/wire_payloads/anthropic_compat__native_orphan.json

@@ -1,70 +0,0 @@
-{
-  "cache_control": {
-    "type": "ephemeral"
-  },
-  "extra_body": {
-    "chat_template_kwargs": {
-      "enable_thinking": true,
-      "reasoning_effort": "high"
-    }
-  },
-  "max_tokens": 4096,
-  "messages": [
-    {
-      "content": "Think about the weather.",
-      "role": "user"
-    },
-    {
-      "content": [
-        {
-          "signature": "sig-abc",
-          "thinking": "The user wants weather.",
-          "type": "thinking"
-        },
-        {
-          "text": "Let me check.",
-          "type": "text"
-        },
-        {
-          "id": "call_1",
-          "input": {
-            "city": "Paris"
-          },
-          "name": "get_weather",
-          "type": "tool_use"
-        }
-      ],
-      "role": "assistant"
-    },
-    {
-      "content": [
-        {
-          "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
-          "is_error": true,
-          "tool_use_id": "call_1",
-          "type": "tool_result"
-        }
-      ],
-      "role": "user"
-    }
-  ],
-  "model": "qwen3.6-27b",
-  "temperature": 0.5,
-  "tools": [
-    {
-      "description": "Look up the weather for a city.",
-      "input_schema": {
-        "properties": {
-          "city": {
-            "type": "string"
-          }
-        },
-        "required": [
-          "city"
-        ],
-        "type": "object"
-      },
-      "name": "get_weather"
-    }
-  ]
-}

tests/data/wire_payloads/anthropic_compat__native_reasoning.json

@@ -1,69 +0,0 @@
-{
-  "cache_control": {
-    "type": "ephemeral"
-  },
-  "extra_body": {
-    "chat_template_kwargs": {
-      "enable_thinking": true,
-      "reasoning_effort": "high"
-    }
-  },
-  "max_tokens": 4096,
-  "messages": [
-    {
-      "content": "Think about the weather.",
-      "role": "user"
-    },
-    {
-      "content": [
-        {
-          "signature": "sig-abc",
-          "thinking": "The user wants weather.",
-          "type": "thinking"
-        },
-        {
-          "text": "Let me check.",
-          "type": "text"
-        },
-        {
-          "id": "call_1",
-          "input": {
-            "city": "Paris"
-          },
-          "name": "get_weather",
-          "type": "tool_use"
-        }
-      ],
-      "role": "assistant"
-    },
-    {
-      "content": [
-        {
-          "content": "18C, clear.",
-          "tool_use_id": "call_1",
-          "type": "tool_result"
-        }
-      ],
-      "role": "user"
-    }
-  ],
-  "model": "qwen3.6-27b",
-  "temperature": 0.5,
-  "tools": [
-    {
-      "description": "Look up the weather for a city.",
-      "input_schema": {
-        "properties": {
-          "city": {
-            "type": "string"
-          }
-        },
-        "required": [
-          "city"
-        ],
-        "type": "object"
-      },
-      "name": "get_weather"
-    }
-  ]
-}

tests/data/wire_payloads/anthropic_compat__operator_system.json

@@ -1,63 +0,0 @@
-{
-  "cache_control": {
-    "type": "ephemeral"
-  },
-  "extra_body": {
-    "chat_template_kwargs": {
-      "enable_thinking": true,
-      "reasoning_effort": "high"
-    }
-  },
-  "max_tokens": 4096,
-  "messages": [
-    {
-      "content": "Run the deploy.",
-      "role": "user"
-    },
-    {
-      "content": [
-        {
-          "id": "call_1",
-          "input": {},
-          "name": "deploy",
-          "type": "tool_use"
-        }
-      ],
-      "role": "assistant"
-    },
-    {
-      "content": [
-        {
-          "content": "deployed",
-          "tool_use_id": "call_1",
-          "type": "tool_result"
-        },
-        {
-          "text": "Great, what's next?",
-          "type": "text"
-        }
-      ],
-      "role": "user"
-    }
-  ],
-  "model": "qwen3.6-27b",
-  "system": "Output-guard: deploy output looked clean.",
-  "temperature": 0.5,
-  "tools": [
-    {
-      "description": "Look up the weather for a city.",
-      "input_schema": {
-        "properties": {
-          "city": {
-            "type": "string"
-          }
-        },
-        "required": [
-          "city"
-        ],
-        "type": "object"
-      },
-      "name": "get_weather"
-    }
-  ]
-}

tests/data/wire_payloads/anthropic_compat__text.json

@@ -1,33 +0,0 @@
-{
-  "cache_control": {
-    "type": "ephemeral"
-  },
-  "extra_body": {
-    "chat_template_kwargs": {
-      "enable_thinking": true,
-      "reasoning_effort": "high"
-    }
-  },
-  "max_tokens": 4096,
-  "messages": [
-    {
-      "content": "Hi there.",
-      "role": "user"
-    },
-    {
-      "content": [
-        {
-          "text": "Hello! How can I help?",
-          "type": "text"
-        }
-      ],
-      "role": "assistant"
-    },
-    {
-      "content": "What's the weather in Paris?",
-      "role": "user"
-    }
-  ],
-  "model": "qwen3.6-27b",
-  "temperature": 0.5
-}

tests/data/wire_payloads/anthropic_compat__toolcall_complete.json

@@ -1,69 +0,0 @@
-{
-  "cache_control": {
-    "type": "ephemeral"
-  },
-  "extra_body": {
-    "chat_template_kwargs": {
-      "enable_thinking": true,
-      "reasoning_effort": "high"
-    }
-  },
-  "max_tokens": 4096,
-  "messages": [
-    {
-      "content": "Weather in Paris?",
-      "role": "user"
-    },
-    {
-      "content": [
-        {
-          "id": "call_1",
-          "input": {
-            "city": "Paris"
-          },
-          "name": "get_weather",
-          "type": "tool_use"
-        }
-      ],
-      "role": "assistant"
-    },
-    {
-      "content": [
-        {
-          "content": "18C, clear.",
-          "tool_use_id": "call_1",
-          "type": "tool_result"
-        }
-      ],
-      "role": "user"
-    },
-    {
-      "content": [
-        {
-          "text": "It's 18C and clear in Paris.",
-          "type": "text"
-        }
-      ],
-      "role": "assistant"
-    }
-  ],
-  "model": "qwen3.6-27b",
-  "temperature": 0.5,
-  "tools": [
-    {
-      "description": "Look up the weather for a city.",
-      "input_schema": {
-        "properties": {
-          "city": {
-            "type": "string"
-          }
-        },
-        "required": [
-          "city"
-        ],
-        "type": "object"
-      },
-      "name": "get_weather"
-    }
-  ]
-}

tests/data/wire_payloads/anthropic_compat__trailing_orphan.json

@@ -1,61 +0,0 @@
-{
-  "cache_control": {
-    "type": "ephemeral"
-  },
-  "extra_body": {
-    "chat_template_kwargs": {
-      "enable_thinking": true,
-      "reasoning_effort": "high"
-    }
-  },
-  "max_tokens": 4096,
-  "messages": [
-    {
-      "content": "Weather in Paris?",
-      "role": "user"
-    },
-    {
-      "content": [
-        {
-          "id": "call_1",
-          "input": {
-            "city": "Paris"
-          },
-          "name": "get_weather",
-          "type": "tool_use"
-        }
-      ],
-      "role": "assistant"
-    },
-    {
-      "content": [
-        {
-          "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
-          "is_error": true,
-          "tool_use_id": "call_1",
-          "type": "tool_result"
-        }
-      ],
-      "role": "user"
-    }
-  ],
-  "model": "qwen3.6-27b",
-  "temperature": 0.5,
-  "tools": [
-    {
-      "description": "Look up the weather for a city.",
-      "input_schema": {
-        "properties": {
-          "city": {
-            "type": "string"
-          }
-        },
-        "required": [
-          "city"
-        ],
-        "type": "object"
-      },
-      "name": "get_weather"
-    }
-  ]
-}

tests/data/wire_payloads/anthropic_hoist__mid_orphan.json

@@ -37,7 +37,7 @@
           "type": "tool_result"
         },
         {
-          "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+          "content": "Tool execution was cancelled.",
           "is_error": true,
           "tool_use_id": "call_2",
           "type": "tool_result"

tests/data/wire_payloads/anthropic_hoist__native_orphan.json

@@ -33,7 +33,7 @@
     {
       "content": [
         {
-          "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+          "content": "Tool execution was cancelled.",
           "is_error": true,
           "tool_use_id": "call_1",
           "type": "tool_result"

tests/data/wire_payloads/anthropic_hoist__trailing_orphan.json

@@ -24,7 +24,7 @@
     {
       "content": [
         {
-          "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+          "content": "Tool execution was cancelled.",
           "is_error": true,
           "tool_use_id": "call_1",
           "type": "tool_result"

tests/data/wire_payloads/anthropic_native__mid_orphan.json

@@ -37,7 +37,7 @@
           "type": "tool_result"
         },
         {
-          "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+          "content": "Tool execution was cancelled.",
           "is_error": true,
           "tool_use_id": "call_2",
           "type": "tool_result"

tests/data/wire_payloads/anthropic_native__native_orphan.json

@@ -33,7 +33,7 @@
     {
       "content": [
         {
-          "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+          "content": "Tool execution was cancelled.",
           "is_error": true,
           "tool_use_id": "call_1",
           "type": "tool_result"

tests/data/wire_payloads/anthropic_native__trailing_orphan.json

@@ -24,7 +24,7 @@
     {
       "content": [
         {
-          "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+          "content": "Tool execution was cancelled.",
           "is_error": true,
           "tool_use_id": "call_1",
           "type": "tool_result"

tests/data/wire_payloads/google__mid_orphan.json

@@ -33,7 +33,7 @@
       "tool_call_id": "call_1"
     },
     {
-      "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+      "content": "Tool execution was cancelled.",
       "role": "tool",
       "tool_call_id": "call_2"
     },
@@ -43,7 +43,6 @@
     }
   ],
   "model": "gemini-2.5-pro",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/google__multipart.json

@@ -18,7 +18,6 @@
     }
   ],
   "model": "gemini-2.5-pro",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/google__native_orphan.json

@@ -20,13 +20,12 @@
       ]
     },
     {
-      "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+      "content": "Tool execution was cancelled.",
       "role": "tool",
       "tool_call_id": "call_1"
     }
   ],
   "model": "gemini-2.5-pro",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/google__native_reasoning.json

@@ -26,7 +26,6 @@
     }
   ],
   "model": "gemini-2.5-pro",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/google__operator_system.json

@@ -34,7 +34,6 @@
     }
   ],
   "model": "gemini-2.5-pro",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/google__text.json

@@ -15,7 +15,6 @@
     }
   ],
   "model": "gemini-2.5-pro",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/google__toolcall_complete.json

@@ -30,7 +30,6 @@
     }
   ],
   "model": "gemini-2.5-pro",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/google__trailing_orphan.json

@@ -20,13 +20,12 @@
       ]
     },
     {
-      "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+      "content": "Tool execution was cancelled.",
       "role": "tool",
       "tool_call_id": "call_1"
     }
   ],
   "model": "gemini-2.5-pro",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/openai_chat__mid_orphan.json

@@ -33,7 +33,7 @@
       "tool_call_id": "call_1"
     },
     {
-      "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+      "content": "Tool execution was cancelled.",
       "role": "tool",
       "tool_call_id": "call_2"
     },
@@ -43,7 +43,6 @@
     }
   ],
   "model": "gpt-4o-mini",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/openai_chat__multipart.json

@@ -18,7 +18,6 @@
     }
   ],
   "model": "gpt-4o-mini",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/openai_chat__native_orphan.json

@@ -20,13 +20,12 @@
       ]
     },
     {
-      "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+      "content": "Tool execution was cancelled.",
       "role": "tool",
       "tool_call_id": "call_1"
     }
   ],
   "model": "gpt-4o-mini",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/openai_chat__native_reasoning.json

@@ -26,7 +26,6 @@
     }
   ],
   "model": "gpt-4o-mini",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/openai_chat__operator_system.json

@@ -34,7 +34,6 @@
     }
   ],
   "model": "gpt-4o-mini",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/openai_chat__text.json

@@ -15,7 +15,6 @@
     }
   ],
   "model": "gpt-4o-mini",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/openai_chat__toolcall_complete.json

@@ -30,7 +30,6 @@
     }
   ],
   "model": "gpt-4o-mini",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/openai_chat__trailing_orphan.json

@@ -20,13 +20,12 @@
       ]
     },
     {
-      "content": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+      "content": "Tool execution was cancelled.",
       "role": "tool",
       "tool_call_id": "call_1"
     }
   ],
   "model": "gpt-4o-mini",
-  "reasoning_effort": "medium",
   "stream": true,
   "stream_options": {
     "include_usage": true

tests/data/wire_payloads/openai_responses__mid_orphan.json

@@ -27,7 +27,7 @@
     },
     {
       "call_id": "call_2",
-      "output": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+      "output": "Tool execution was cancelled.",
       "type": "function_call_output"
     },
     {

tests/data/wire_payloads/openai_responses__native_orphan.json

@@ -21,7 +21,7 @@
     },
     {
       "call_id": "call_1",
-      "output": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+      "output": "Tool execution was cancelled.",
       "type": "function_call_output"
     }
   ],

tests/data/wire_payloads/openai_responses__trailing_orphan.json

@@ -16,7 +16,7 @@
     },
     {
       "call_id": "call_1",
-      "output": "Tool execution was cancelled. Outcome UNKNOWN — this call may have begun executing before the generation was stopped; do not assume it did not run, and reconcile before re-issuing it.",
+      "output": "Tool execution was cancelled.",
       "type": "function_call_output"
     }
   ],

tests/test_app_js.py

@@ -530,17 +530,15 @@ def test_phase8_appendtooloutput_dispatches_mcp_error_before_renderer() -> None:
     end = _pane_method_offset(body, "sendMessage")
     fn = body[start:end]
     parse_idx = fn.find("tryParseMcpError(")
-    # The plain-output render is the shared renderCollapsibleOutput helper; the
-    # ordering invariant is unchanged — MCP dispatch must precede it.
-    render_idx = fn.find("renderCollapsibleOutput(")
+    render_idx = fn.find("renderToolOutput(")
     assert parse_idx >= 0, (
         "appendToolOutput must call tryParseMcpError on the error path "
-        "before the plain renderer, otherwise the consent card never "
+        "before renderToolOutput, otherwise the consent card never "
         "replaces the plain JSON output."
     )
-    assert render_idx >= 0, "renderCollapsibleOutput call must remain present"
+    assert render_idx >= 0, "renderToolOutput call must remain present"
     assert parse_idx < render_idx, (
-        "tryParseMcpError must run BEFORE the plain renderer so the "
+        "tryParseMcpError must run BEFORE renderToolOutput so the "
         "interactive card path takes precedence over plain rendering."
     )
 
@@ -739,10 +737,7 @@ def test_dashboard_is_the_main_pane_body() -> None:
     body = _INDEX_HTML.read_text(encoding="utf-8")
     assert 'id="main"' in body, "the dashboard content lives in #main (the Dashboard pane body)."
     start = body.index('id="main"')
-    # Window spans the launcher (composer + options) through the workstreams
-    # table — it grows as launcher options are added (e.g. the project picker),
-    # so the bound just needs to keep BOTH inside #main, not be tight.
-    chunk = body[start : start + 4500]
+    chunk = body[start : start + 4000]
     assert 'id="dashboard-input"' in chunk and 'id="dash-ws-table"' in chunk, (
         "#main must hold the new-session launcher + the workstreams table."
     )
@@ -1589,89 +1584,6 @@ def test_early_paint_tool_pending_wiring() -> None:
     assert "if (!announced) this.messagesEl.appendChild(block);" in body
 
 
-def test_task_agent_steps_never_escape_their_card() -> None:
-    """A task agent's sub-tool steps (``parent_call_id`` stamped) must nest in
-    the task card, never render as top-level rows that look like the main
-    harness issued them.  Two seams keep that true; this guards both against a
-    rename/deletion:
-
-    1. ``tool_info`` routes through ``_routeAgentItems`` first — a sub-tool
-       auto-resolved by policy / "Always" arrives as a ``tool_info`` and must
-       nest, not paint a duplicate top-level block (Copilot review on #732).
-    2. A child step whose ``task_agent`` row hasn't painted yet (the 4-wide
-       tool pool's ordering window) is BUFFERED and flushed when the row lands,
-       instead of escaping to top-level; the card also survives the parent
-       row's pending->resolved rebuild.
-    3. SAFETY VALVE: a buffered step whose parent row NEVER paints (an id-
-       correlation mismatch / aborted agent) is escaped to a top-level row after
-       a grace window, so it stays VISIBLE rather than buffered forever.
-    """
-    body = _INTERACTIVE_JS.read_text(encoding="utf-8")
-    # 1. tool_info nests via the same router as tool_pending / approve_request.
-    info = body[body.index('case "tool_info":') : body.index('case "approve_request":')]
-    assert 'this._routeAgentItems(evt.items, "info")' in info, (
-        "tool_info must route a parent-tagged sub-tool into the task card "
-        "before any top-level showInlineToolBlock fallback."
-    )
-    # 2. _routeAgentItems buffers an orphan child (instead of returning false,
-    #    which escapes it to top-level) when the parent card isn't painted yet.
-    route = body[
-        _pane_method_offset(body, "_routeAgentItems") : _pane_method_offset(
-            body, "_ensureAgentCard"
-        )
-    ]
-    assert "_bufferAgentOrphan(parentId, items, mode)" in route, (
-        "a parent-tagged child with no card yet must buffer, not fall through to a top-level paint."
-    )
-    # The buffer / flush / escape / relink helpers exist.
-    assert "_bufferAgentOrphan(parentId, items, mode) {" in body
-    assert "_flushAgentOrphans(parentIds) {" in body
-    assert "_escapeAgentOrphans(parentId) {" in body
-    assert "_relinkAgentCards(items) {" in body
-    assert body.count("this._relinkAgentCards(") >= 2, (
-        "both announceToolBlock and showInlineToolBlock must relink + flush so "
-        "a buffered step nests as soon as a tool row appears."
-    )
-    # 3. Safety valve: _bufferAgentOrphan arms a grace timer to _escapeAgentOrphans
-    #    so a never-painting parent's steps can't vanish (or leak) — they escape
-    #    back to a visible top-level paint.
-    buf = body[
-        _pane_method_offset(body, "_bufferAgentOrphan") : _pane_method_offset(
-            body, "_flushAgentOrphans"
-        )
-    ]
-    assert "setTimeout(" in buf and "_escapeAgentOrphans(parentId)" in buf, (
-        "a buffered orphan must arm a grace-window escape so it never stays "
-        "buffered (invisible) forever."
-    )
-    escape = body[
-        _pane_method_offset(body, "_escapeAgentOrphans") : _pane_method_offset(
-            body, "_relinkAgentCards"
-        )
-    ]
-    assert "announceToolBlock(" in escape, (
-        "the escape valve must render the steps top-level (visible), the "
-        "pre-buffer behaviour, rather than dropping them."
-    )
-    # Flush is targeted to the just-painted parents, not the whole map.
-    flush = body[
-        _pane_method_offset(body, "_flushAgentOrphans") : _pane_method_offset(
-            body, "_escapeAgentOrphans"
-        )
-    ]
-    assert "parentIds.forEach" in flush
-    # _ensureAgentCard re-attaches a DETACHED card across a parent-row rebuild,
-    # but builds fresh on a still-attached (cross-turn reused) call_id rather
-    # than stealing the prior agent's steps.
-    ensure = body[
-        _pane_method_offset(body, "_ensureAgentCard") : _pane_method_offset(
-            body, "_bufferAgentOrphan"
-        )
-    ]
-    assert "!card.wrap.isConnected" in ensure
-    assert "parentRow.appendChild(card.wrap);" in ensure
-
-
 def test_risk_level_normalized_before_dom_interpolation() -> None:
     """Server-supplied ``risk_level`` lands in className / data-risk strings the
     verdict + warning CSS depend on, so every interpolation must funnel through
@@ -1735,25 +1647,3 @@ def test_early_paint_screen_reader_announce() -> None:
     assert "toolAnnounce(_toolAnnounceText(list))" in body
     assert 'block.setAttribute("aria-busy", "true")' in body
     assert 'block.removeAttribute("aria-busy")' in body
-
-
-def test_global_stream_recovery_floor_and_render_coalescing() -> None:
-    """Perf-audit P0/P1 for the Tier-1 global stream.  The server's recovery
-    events for a truncated reconnect gap (``node_snapshot`` as the floor,
-    ``replay_truncated`` as the marker) used to fall through the handler
-    silently — workstreams created during a long hidden-tab gap never
-    rendered again, and missed ``ws_closed`` left ghost rows forever.  A
-    malformed frame is the same permanent drift (the cursor advances before
-    the parse), so it resyncs too.  ``fireRender`` is rAF-coalesced: every
-    ``ws_state`` (≥2 per tool round per workstream) used to trigger a
-    synchronous full rail rebuild."""
-    body = _APP_JS.read_text(encoding="utf-8")
-    assert 'data.type === "node_snapshot"' in body
-    assert 'data.type === "replay_truncated"' in body
-    assert "function applyRosterSnapshot(" in body
-    assert "function resyncRoster(" in body
-    assert "malformed frame" in body
-    fire = body.index("function fireRender()")
-    assert "requestAnimationFrame(" in body[fire : fire + 700], (
-        "fireRender must coalesce subscriber repaints to one per frame"
-    )

tests/test_bootstrap.py

@@ -0,0 +1,683 @@
+"""Tests for the bootstrap wizard module."""
+
+from __future__ import annotations
+
+import os
+import socket
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+from turnstone.bootstrap import (
+    SYSTEM_PROMPT,
+    TOOLS,
+    _BootstrapLLM,
+    _FinishError,
+    _mask_secrets,
+    _tool_check_docker,
+    _tool_check_port,
+    _tool_finish,
+    _tool_generate_secret,
+    _tool_read_file,
+    _tool_validate_api_key,
+    _tool_write_compose,
+    _tool_write_file,
+    execute_tool,
+)
+
+# ---------------------------------------------------------------------------
+# Tool function tests
+# ---------------------------------------------------------------------------
+
+
+class TestReadFile:
+    def test_existing_file(self, tmp_path: Path) -> None:
+        f = tmp_path / "test.txt"
+        f.write_text("hello world")
+        result = _tool_read_file(tmp_path, {"path": "test.txt"})
+        assert result == "hello world"
+
+    def test_missing_file(self, tmp_path: Path) -> None:
+        result = _tool_read_file(tmp_path, {"path": "nope.txt"})
+        assert "Error: file not found" in result
+
+    def test_nested_path(self, tmp_path: Path) -> None:
+        sub = tmp_path / "sub"
+        sub.mkdir()
+        f = sub / "nested.txt"
+        f.write_text("nested content")
+        result = _tool_read_file(tmp_path, {"path": "sub/nested.txt"})
+        assert result == "nested content"
+
+    def test_path_traversal_blocked(self, tmp_path: Path) -> None:
+        result = _tool_read_file(tmp_path, {"path": "../../etc/passwd"})
+        assert "escapes project directory" in result
+
+    def test_absolute_path_blocked(self, tmp_path: Path) -> None:
+        result = _tool_read_file(tmp_path, {"path": "/etc/passwd"})
+        assert "escapes project directory" in result
+
+
+class TestWriteFile:
+    def test_write_confirmed(self, tmp_path: Path) -> None:
+        with patch("builtins.input", return_value="y"):
+            result = _tool_write_file(tmp_path, {"path": "out.txt", "content": "data\n"})
+        assert "written successfully" in result
+        assert (tmp_path / "out.txt").read_text() == "data\n"
+
+    def test_write_declined(self, tmp_path: Path) -> None:
+        with patch("builtins.input", return_value="n"):
+            result = _tool_write_file(tmp_path, {"path": "out.txt", "content": "data\n"})
+        assert "declined" in result
+        assert not (tmp_path / "out.txt").exists()
+
+    def test_write_creates_parent_dirs(self, tmp_path: Path) -> None:
+        with patch("builtins.input", return_value="y"):
+            result = _tool_write_file(tmp_path, {"path": "a/b/c.txt", "content": "deep\n"})
+        assert "written successfully" in result
+        assert (tmp_path / "a" / "b" / "c.txt").read_text() == "deep\n"
+
+    def test_sh_files_are_executable(self, tmp_path: Path) -> None:
+        with patch("builtins.input", return_value="y"):
+            _tool_write_file(tmp_path, {"path": "setup.sh", "content": "#!/bin/bash\n"})
+        mode = (tmp_path / "setup.sh").stat().st_mode
+        assert mode & 0o110  # user + group executable, not world
+
+    def test_path_traversal_blocked(self, tmp_path: Path) -> None:
+        result = _tool_write_file(tmp_path, {"path": "../../escape.txt", "content": "bad\n"})
+        assert "escapes project directory" in result
+
+    def test_default_enter_confirms(self, tmp_path: Path) -> None:
+        with patch("builtins.input", return_value=""):
+            result = _tool_write_file(tmp_path, {"path": "ok.txt", "content": "ok\n"})
+        assert "written successfully" in result
+
+    def test_duplicate_write_skipped(self, tmp_path: Path) -> None:
+        (tmp_path / "dup.txt").write_text("same\n")
+        result = _tool_write_file(tmp_path, {"path": "dup.txt", "content": "same\n"})
+        assert "already exists" in result
+
+    def test_different_content_still_prompts(self, tmp_path: Path) -> None:
+        (tmp_path / "changed.txt").write_text("old\n")
+        with patch("builtins.input", return_value="y"):
+            result = _tool_write_file(tmp_path, {"path": "changed.txt", "content": "new\n"})
+        assert "written successfully" in result
+        assert (tmp_path / "changed.txt").read_text() == "new\n"
+
+
+class TestWriteCompose:
+    def test_writes_compose_file(self, tmp_path: Path) -> None:
+        with patch("builtins.input", return_value="y"):
+            result = _tool_write_compose(tmp_path, {})
+        assert "written successfully" in result
+        assert "ghcr.io" in result
+        content = (tmp_path / "compose.yaml").read_text()
+        assert "ghcr.io/turnstonelabs/turnstone" in content
+        assert "TURNSTONE_IMAGE_TAG" in content
+        # The compose mounts ./Caddyfile and ./searxng, so the wizard must write
+        # both alongside — guards the extra writes and the pyproject wheel-include.
+        caddyfile = (tmp_path / "Caddyfile").read_text()
+        assert "reverse_proxy console:8090" in caddyfile
+        searxng_cfg = (tmp_path / "searxng" / "settings.yml").read_text()
+        assert "json" in searxng_cfg  # the bundled config enables the JSON API
+
+    def test_user_declines(self, tmp_path: Path) -> None:
+        with patch("builtins.input", return_value="n"):
+            result = _tool_write_compose(tmp_path, {})
+        assert "declined" in result
+        assert not (tmp_path / "compose.yaml").exists()
+
+    def test_identical_content_skipped(self, tmp_path: Path) -> None:
+        # Write it once
+        with patch("builtins.input", return_value="y"):
+            _tool_write_compose(tmp_path, {})
+        # Second call should skip
+        result = _tool_write_compose(tmp_path, {})
+        assert "identical content" in result
+
+    def test_no_build_blocks(self, tmp_path: Path) -> None:
+        with patch("builtins.input", return_value="y"):
+            _tool_write_compose(tmp_path, {})
+        content = (tmp_path / "compose.yaml").read_text()
+        assert "build:" not in content
+        assert "dockerfile:" not in content.lower()
+
+    def test_overwrites_different_content(self, tmp_path: Path) -> None:
+        (tmp_path / "compose.yaml").write_text("old content\n")
+        with patch("builtins.input", return_value="y"):
+            result = _tool_write_compose(tmp_path, {})
+        assert "written successfully" in result
+        content = (tmp_path / "compose.yaml").read_text()
+        assert "ghcr.io" in content
+
+    def test_no_local_image_references(self, tmp_path: Path) -> None:
+        with patch("builtins.input", return_value="y"):
+            _tool_write_compose(tmp_path, {})
+        content = (tmp_path / "compose.yaml").read_text()
+        assert "turnstone:local" not in content
+
+
+class TestGenerateSecret:
+    def test_default_length(self) -> None:
+        secret = _tool_generate_secret({})
+        assert len(secret) == 64  # 32 bytes -> 64 hex chars
+
+    def test_custom_length(self) -> None:
+        secret = _tool_generate_secret({"length": 16})
+        assert len(secret) == 32
+
+    def test_uniqueness(self) -> None:
+        s1 = _tool_generate_secret({})
+        s2 = _tool_generate_secret({})
+        assert s1 != s2
+
+    def test_invalid_length_fallback(self) -> None:
+        secret = _tool_generate_secret({"length": -1})
+        assert len(secret) == 64  # falls back to 32 bytes
+
+    def test_excessive_length_capped(self) -> None:
+        secret = _tool_generate_secret({"length": 99999})
+        assert len(secret) == 64  # falls back to 32 bytes
+
+
+class TestCheckPort:
+    def test_available_port(self) -> None:
+        # Pick a random high port that's likely free
+        result = _tool_check_port({"port": 59123})
+        assert "AVAILABLE" in result or "IN USE" in result
+
+    def test_in_use_port(self) -> None:
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+            sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+            sock.bind(("127.0.0.1", 0))
+            port = sock.getsockname()[1]
+            sock.listen(1)
+            result = _tool_check_port({"port": port})
+        assert "IN USE" in result
+
+    def test_invalid_port(self) -> None:
+        result = _tool_check_port({"port": -1})
+        assert "Error" in result
+
+    def test_port_zero(self) -> None:
+        result = _tool_check_port({"port": 0})
+        assert "Error" in result
+
+
+class TestCheckDocker:
+    def test_docker_installed(self) -> None:
+        mock_docker = MagicMock()
+        mock_docker.returncode = 0
+        mock_docker.stdout = "24.0.7"
+
+        mock_compose = MagicMock()
+        mock_compose.returncode = 0
+        mock_compose.stdout = "2.24.5"
+
+        with patch("subprocess.run", side_effect=[mock_docker, mock_compose]):
+            result = _tool_check_docker({})
+        assert "Docker: installed" in result
+        assert "Docker Compose: installed" in result
+
+    def test_docker_not_installed(self) -> None:
+        with patch("subprocess.run", side_effect=FileNotFoundError):
+            result = _tool_check_docker({})
+        assert "NOT installed" in result or "NOT available" in result
+
+    def test_docker_daemon_not_running(self) -> None:
+        mock_docker = MagicMock()
+        mock_docker.returncode = 1
+        mock_docker.stderr = "Cannot connect to the Docker daemon"
+
+        mock_compose = MagicMock()
+        mock_compose.returncode = 1
+
+        with patch("subprocess.run", side_effect=[mock_docker, mock_compose]):
+            result = _tool_check_docker({})
+        assert "NOT running" in result
+
+
+class TestValidateApiKey:
+    def test_openai_success(self) -> None:
+        mock_client = MagicMock()
+        mock_client.models.list.return_value = []
+        with patch("openai.OpenAI", return_value=mock_client):
+            result = _tool_validate_api_key({"provider": "openai", "api_key": "sk-test"})
+        assert "Success" in result
+
+    def test_openai_failure(self) -> None:
+        with patch("openai.OpenAI") as mock_cls:
+            mock_cls.return_value.models.list.side_effect = Exception("Invalid key")
+            result = _tool_validate_api_key({"provider": "openai", "api_key": "bad"})
+        assert "Failed" in result
+
+    def test_unknown_provider(self) -> None:
+        result = _tool_validate_api_key({"provider": "unknown", "api_key": "x"})
+        assert "unknown" in result
+
+
+class TestExecuteTool:
+    def test_unknown_tool(self, tmp_path: Path) -> None:
+        result = execute_tool("nonexistent", {}, tmp_path)
+        assert "unknown tool" in result
+
+    def test_dispatches_correctly(self, tmp_path: Path) -> None:
+        f = tmp_path / "hello.txt"
+        f.write_text("hi")
+        result = execute_tool("read_file", {"path": "hello.txt"}, tmp_path)
+        assert result == "hi"
+
+    def test_finish_raises(self, tmp_path: Path) -> None:
+        import pytest
+
+        with pytest.raises(_FinishError, match="All done"):
+            execute_tool("finish", {"summary": "All done"}, tmp_path)
+
+
+class TestFinishTool:
+    def test_raises_with_summary(self) -> None:
+        import pytest
+
+        with pytest.raises(_FinishError) as exc_info:
+            _tool_finish({"summary": "Configured production deployment."})
+        assert exc_info.value.summary == "Configured production deployment."
+
+    def test_default_summary(self) -> None:
+        import pytest
+
+        with pytest.raises(_FinishError) as exc_info:
+            _tool_finish({})
+        assert exc_info.value.summary == "Setup complete."
+
+
+# ---------------------------------------------------------------------------
+# Secret masking tests
+# ---------------------------------------------------------------------------
+
+
+class TestMaskSecrets:
+    def test_masks_api_key(self) -> None:
+        text = "OPENAI_API_KEY=sk-1234567890abcdef"
+        result = _mask_secrets(text)
+        assert "sk-1" in result
+        assert "cdef" in result
+        assert "1234567890abcde" not in result
+
+    def test_preserves_comments(self) -> None:
+        text = "# OPENAI_API_KEY=sk-1234567890abcdef"
+        result = _mask_secrets(text)
+        assert result == text
+
+    def test_preserves_short_values(self) -> None:
+        text = "TOKEN=short"
+        result = _mask_secrets(text)
+        assert result == text
+
+    def test_preserves_non_sensitive(self) -> None:
+        text = "MODEL=gpt-5.4"
+        result = _mask_secrets(text)
+        assert result == text
+
+
+# ---------------------------------------------------------------------------
+# Message conversion tests (Anthropic)
+# ---------------------------------------------------------------------------
+
+
+class TestAnthropicConversion:
+    """Test the Anthropic message/tool conversion inside _BootstrapLLM."""
+
+    def _make_llm(self) -> _BootstrapLLM:
+        return _BootstrapLLM("anthropic", MagicMock(), "test-model")
+
+    def test_tool_format_conversion(self) -> None:
+        """OpenAI tool format should convert to Anthropic format."""
+        llm = self._make_llm()
+        # The conversion happens inside _complete_anthropic; we test indirectly
+        # by checking the tools passed to the mock client
+        mock_response = MagicMock()
+        mock_response.content = [MagicMock(type="text", text="hello")]
+        mock_response.stop_reason = "end_turn"
+        llm.client.messages.create.return_value = mock_response
+
+        llm.complete(
+            [{"role": "system", "content": "sys"}, {"role": "user", "content": "hi"}],
+            TOOLS[:1],  # Just read_file
+        )
+
+        call_kwargs = llm.client.messages.create.call_args[1]
+        api_tools = call_kwargs["tools"]
+        assert len(api_tools) == 1
+        assert api_tools[0]["name"] == "read_file"
+        assert "input_schema" in api_tools[0]
+        assert "description" in api_tools[0]
+
+    def test_system_message_extraction(self) -> None:
+        """System message should be extracted to system parameter."""
+        llm = self._make_llm()
+        mock_response = MagicMock()
+        mock_response.content = [MagicMock(type="text", text="ok")]
+        mock_response.stop_reason = "end_turn"
+        llm.client.messages.create.return_value = mock_response
+
+        llm.complete(
+            [{"role": "system", "content": "test system"}, {"role": "user", "content": "hi"}],
+            [],
+        )
+
+        call_kwargs = llm.client.messages.create.call_args[1]
+        assert call_kwargs["system"] == "test system"
+        # System should NOT appear in messages
+        for msg in call_kwargs["messages"]:
+            assert msg["role"] != "system"
+
+    def test_tool_result_conversion(self) -> None:
+        """OpenAI tool result messages should convert to Anthropic format."""
+        llm = self._make_llm()
+        mock_response = MagicMock()
+        mock_response.content = [MagicMock(type="text", text="got it")]
+        mock_response.stop_reason = "end_turn"
+        llm.client.messages.create.return_value = mock_response
+
+        messages = [
+            {"role": "system", "content": "sys"},
+            {"role": "user", "content": "hi"},
+            {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {
+                        "id": "tc_1",
+                        "type": "function",
+                        "function": {"name": "check_docker", "arguments": "{}"},
+                    }
+                ],
+            },
+            {
+                "role": "tool",
+                "tool_call_id": "tc_1",
+                "content": "Docker: installed",
+            },
+        ]
+        llm.complete(messages, TOOLS)
+
+        call_kwargs = llm.client.messages.create.call_args[1]
+        api_messages = call_kwargs["messages"]
+
+        # Find the tool_result message
+        tool_result_found = False
+        for msg in api_messages:
+            if msg["role"] == "user" and isinstance(msg.get("content"), list):
+                for block in msg["content"]:
+                    if isinstance(block, dict) and block.get("type") == "tool_result":
+                        assert block["tool_use_id"] == "tc_1"
+                        assert block["content"] == "Docker: installed"
+                        tool_result_found = True
+        assert tool_result_found
+
+    def test_tool_use_blocks_in_assistant(self) -> None:
+        """Assistant messages with tool_calls should convert to content blocks."""
+        llm = self._make_llm()
+        mock_response = MagicMock()
+        mock_response.content = [MagicMock(type="text", text="ok")]
+        mock_response.stop_reason = "end_turn"
+        llm.client.messages.create.return_value = mock_response
+
+        messages = [
+            {"role": "system", "content": "sys"},
+            {"role": "user", "content": "hi"},
+            {
+                "role": "assistant",
+                "content": "Let me check",
+                "tool_calls": [
+                    {
+                        "id": "tc_1",
+                        "type": "function",
+                        "function": {"name": "check_docker", "arguments": "{}"},
+                    }
+                ],
+            },
+            {"role": "tool", "tool_call_id": "tc_1", "content": "ok"},
+        ]
+        llm.complete(messages, TOOLS)
+
+        call_kwargs = llm.client.messages.create.call_args[1]
+        api_messages = call_kwargs["messages"]
+
+        # First message should be user "hi"
+        assert api_messages[0]["role"] == "user"
+        # Second should be assistant with content blocks
+        assistant_msg = api_messages[1]
+        assert assistant_msg["role"] == "assistant"
+        assert isinstance(assistant_msg["content"], list)
+        # Should have text block + tool_use block
+        types = [b["type"] for b in assistant_msg["content"]]
+        assert "text" in types
+        assert "tool_use" in types
+
+
+class TestOpenAICompletion:
+    """Test the OpenAI path of _BootstrapLLM."""
+
+    def test_text_response(self) -> None:
+        llm = _BootstrapLLM("openai", MagicMock(), "gpt-5.4")
+        mock_choice = MagicMock()
+        mock_choice.message.content = "Hello!"
+        mock_choice.message.tool_calls = None
+        mock_choice.finish_reason = "stop"
+        llm.client.chat.completions.create.return_value = MagicMock(choices=[mock_choice])
+
+        content, tool_calls, reason = llm.complete([{"role": "user", "content": "hi"}], TOOLS)
+        assert content == "Hello!"
+        assert tool_calls is None
+        assert reason == "stop"
+
+    def test_tool_call_response(self) -> None:
+        llm = _BootstrapLLM("openai", MagicMock(), "gpt-5.4")
+
+        mock_tc = MagicMock()
+        mock_tc.id = "call_123"
+        mock_tc.function.name = "check_docker"
+        mock_tc.function.arguments = "{}"
+
+        mock_choice = MagicMock()
+        mock_choice.message.content = ""
+        mock_choice.message.tool_calls = [mock_tc]
+        mock_choice.finish_reason = "tool_calls"
+        llm.client.chat.completions.create.return_value = MagicMock(choices=[mock_choice])
+
+        content, tool_calls, reason = llm.complete(
+            [{"role": "user", "content": "check docker"}], TOOLS
+        )
+        assert tool_calls is not None
+        assert len(tool_calls) == 1
+        assert tool_calls[0]["function"]["name"] == "check_docker"
+        assert tool_calls[0]["id"] == "call_123"
+
+    def test_no_content(self) -> None:
+        llm = _BootstrapLLM("openai", MagicMock(), "gpt-5.4")
+        mock_choice = MagicMock()
+        mock_choice.message.content = None
+        mock_choice.message.tool_calls = None
+        mock_choice.finish_reason = "stop"
+        llm.client.chat.completions.create.return_value = MagicMock(choices=[mock_choice])
+
+        content, tool_calls, reason = llm.complete([{"role": "user", "content": "hi"}], [])
+        assert content == ""
+        assert tool_calls is None
+
+
+# ---------------------------------------------------------------------------
+# Conversation loop tests
+# ---------------------------------------------------------------------------
+
+
+class TestConversationLoop:
+    def test_quit_exits(self) -> None:
+        """User typing 'quit' should exit the loop."""
+        llm = MagicMock(spec=_BootstrapLLM)
+        llm.complete.return_value = ("What would you like?", None, "stop")
+
+        with patch("builtins.input", return_value="quit"):
+            from turnstone.bootstrap import _run_conversation
+
+            _run_conversation(llm, Path("/tmp"))
+
+    def test_tool_calls_executed(self, tmp_path: Path) -> None:
+        """Tool calls should be executed and results fed back."""
+        llm = MagicMock(spec=_BootstrapLLM)
+        # First call: LLM returns a tool call
+        llm.complete.side_effect = [
+            (
+                "",
+                [
+                    {
+                        "id": "tc_1",
+                        "type": "function",
+                        "function": {"name": "generate_secret", "arguments": "{}"},
+                    }
+                ],
+                "tool_calls",
+            ),
+            # Second call: LLM responds with text after seeing tool result
+            ("Here's your secret!", None, "stop"),
+        ]
+
+        with patch("builtins.input", return_value="quit"):
+            from turnstone.bootstrap import _run_conversation
+
+            _run_conversation(llm, tmp_path)
+
+        # Verify two calls were made
+        assert llm.complete.call_count == 2
+        # Verify tool result was fed back in second call's messages
+        second_call_messages = llm.complete.call_args_list[1][0][0]
+        tool_results = [m for m in second_call_messages if m.get("role") == "tool"]
+        assert len(tool_results) == 1
+        assert tool_results[0]["tool_call_id"] == "tc_1"
+        # Result should be a 64-char hex string
+        assert len(tool_results[0]["content"]) == 64
+
+    def test_empty_input_skipped(self) -> None:
+        """Empty user input should be skipped."""
+        llm = MagicMock(spec=_BootstrapLLM)
+        llm.complete.return_value = ("Ask me something.", None, "stop")
+
+        call_count = 0
+
+        def mock_input(prompt: str = "") -> str:
+            nonlocal call_count
+            call_count += 1
+            if call_count <= 2:
+                return ""  # Empty inputs
+            return "quit"
+
+        with patch("builtins.input", side_effect=mock_input):
+            from turnstone.bootstrap import _run_conversation
+
+            _run_conversation(llm, Path("/tmp"))
+
+    def test_finish_tool_exits_loop(self, tmp_path: Path) -> None:
+        """LLM calling finish tool should exit the conversation cleanly."""
+        llm = MagicMock(spec=_BootstrapLLM)
+        llm.complete.return_value = (
+            "",
+            [
+                {
+                    "id": "tc_fin",
+                    "type": "function",
+                    "function": {
+                        "name": "finish",
+                        "arguments": '{"summary": "All configured."}',
+                    },
+                }
+            ],
+            "tool_calls",
+        )
+
+        from turnstone.bootstrap import _run_conversation
+
+        # Should return without needing user input
+        _run_conversation(llm, tmp_path)
+        assert llm.complete.call_count == 1
+
+
+# ---------------------------------------------------------------------------
+# Interactive startup tests
+# ---------------------------------------------------------------------------
+
+
+class TestProviderDefaults:
+    def test_openai_default_model(self) -> None:
+        from turnstone.bootstrap import _DEFAULT_MODELS
+
+        assert _DEFAULT_MODELS["openai"] == "gpt-5.4"
+
+    def test_anthropic_default_model(self) -> None:
+        from turnstone.bootstrap import _DEFAULT_MODELS
+
+        assert _DEFAULT_MODELS["anthropic"] == "claude-sonnet-4-6"
+
+
+class TestSelectProvider:
+    def test_openai_selection(self) -> None:
+        """Selecting '1' should set up OpenAI."""
+        mock_client = MagicMock()
+        with (
+            patch("builtins.input", side_effect=["1", ""]),
+            patch("getpass.getpass", return_value="sk-test"),
+            patch("openai.OpenAI", return_value=mock_client),
+        ):
+            from turnstone.bootstrap import _select_provider
+
+            provider, client, model = _select_provider()
+        assert provider == "openai"
+        assert model == "gpt-5.4"
+
+    def test_local_selection(self) -> None:
+        """Selecting '3' should set up local/vLLM."""
+        mock_client = MagicMock()
+        # Ensure OPENAI_API_KEY is not in env so we hit the getpass path
+        env = {k: v for k, v in os.environ.items() if k != "OPENAI_API_KEY"}
+        with (
+            patch.dict("os.environ", env, clear=True),
+            patch("builtins.input", side_effect=["3", "http://localhost:8000/v1", "my-model"]),
+            patch("getpass.getpass", return_value="none"),
+            patch("openai.OpenAI", return_value=mock_client),
+        ):
+            from turnstone.bootstrap import _select_provider
+
+            provider, client, model = _select_provider()
+        assert provider == "openai"
+        assert model == "my-model"
+
+
+# ---------------------------------------------------------------------------
+# System prompt and tools sanity checks
+# ---------------------------------------------------------------------------
+
+
+class TestConstants:
+    def test_system_prompt_not_empty(self) -> None:
+        assert len(SYSTEM_PROMPT) > 500
+
+    def test_system_prompt_mentions_turnstone(self) -> None:
+        assert "Turnstone" in SYSTEM_PROMPT
+
+    def test_all_tools_have_required_fields(self) -> None:
+        for tool in TOOLS:
+            assert tool["type"] == "function"
+            func = tool["function"]
+            assert "name" in func
+            assert "description" in func
+            assert "parameters" in func
+            assert func["parameters"]["type"] == "object"
+
+    def test_tool_count(self) -> None:
+        assert len(TOOLS) == 8
+
+    def test_all_tools_have_implementations(self) -> None:
+        from turnstone.bootstrap import TOOL_FUNCTIONS
+
+        for tool in TOOLS:
+            name = tool["function"]["name"]
+            assert name in TOOL_FUNCTIONS, f"Missing implementation for tool: {name}"

tests/test_cancel.py

@@ -1,7 +1,6 @@
 """Tests for generation cancellation (cooperative cancel via threading.Event)."""
 
 import contextlib
-import json
 import threading
 import time
 from dataclasses import dataclass, field
@@ -9,20 +8,8 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
-from turnstone.core.session import (
-    ChatSession,
-    GenerationCancelled,
-    _CancelRef,
-    _effect_status_meta,
-)
-from turnstone.core.trajectory import (
-    EffectStatus,
-    Role,
-    ToolCall,
-    Turn,
-    dicts_from_turns,
-    turn_from_dict,
-)
+from turnstone.core.session import ChatSession, GenerationCancelled, _CancelRef
+from turnstone.core.trajectory import dicts_from_turns, turn_from_dict
 
 
 class NullUI:
@@ -901,19 +888,12 @@ class TestSynthesizeCancelledResults:
         # All emitted as errors so the live UI renders them as
         # ``coord-tool-row-result--error``.
         assert all(tr[3] is True for tr in ui.tool_results)
-        # Reason text propagates as a prefix, now followed by an explicit
-        # UNKNOWN-outcome clause (unknown, never none — see HYPOTHESIS.md):
-        # the call may have begun executing before cancel, so the synthetic
-        # result must not read as "it didn't happen."
-        assert all(tr[2].startswith("Cancelled by user.") for tr in ui.tool_results)
-        assert all("UNKNOWN" in tr[2] for tr in ui.tool_results)
+        # Reason text propagates as the synthetic tool output.
+        assert all(tr[2] == "Cancelled by user." for tr in ui.tool_results)
         # And the message list has the synthesized tool entries
         # (preserves the prior contract).
         tool_msgs = [m for m in dicts_from_turns(session.messages) if m.get("role") == "tool"]
         assert len(tool_msgs) == 2
-        # Typed twin of the prose (Thread A): each synthesized turn is UNKNOWN.
-        tool_turns = [m for m in session.messages if m.role is Role.TOOL]
-        assert tool_turns and all(t.effect_status is EffectStatus.UNKNOWN for t in tool_turns)
 
     def test_skips_calls_already_answered(self, tmp_db):
         ui = self._ui_with_tool_result_tracking()
@@ -972,258 +952,3 @@ class TestSynthesizeCancelledResults:
 
         tool_msgs = [m for m in dicts_from_turns(session.messages) if m.get("role") == "tool"]
         assert len(tool_msgs) == 1
-
-
-class TestTimeoutDisposition:
-    """A tool stopped at its deadline has unobserved side effects, so its
-    result must read UNKNOWN — the same ``unknown, never none`` discipline as
-    cancellation (HYPOTHESIS.md effect-record appendix), applied to timeouts.
-    Read-only timeouts stay a plain failure: an idempotent read has nothing to
-    reconcile, and "reconcile before re-issuing" would be misleading there.
-    """
-
-    def test_bash_timeout_reads_unknown(self):
-        """A bash command is SIGKILL'd at its deadline — the same mid-flight
-        kill as cancel — so it may have run partially or had side effects and
-        must read UNKNOWN, not a flat 'timed out' that invites a blind re-run."""
-        session = _make_session(tool_timeout=1)
-        # Sleeps silently past the 1s deadline → watchdog SIGKILL → TimeoutExpired.
-        call_id, result = session._exec_bash({"call_id": "c1", "command": "sleep 30"})
-        assert call_id == "c1"
-        assert "timed out" in result.lower()
-        assert "UNKNOWN" in result
-        # Typed twin of the prose (Thread A): the producer records UNKNOWN.
-        assert session._tool_status.get("c1") is EffectStatus.UNKNOWN
-
-    def test_mcp_tool_timeout_reads_unknown(self):
-        """An MCP tool is an opaque action — the server may have run it to
-        completion before we stopped waiting, so the outcome reads UNKNOWN."""
-        session = _make_session()
-        session._mcp_client = MagicMock()
-        session._mcp_client.call_tool_sync.side_effect = TimeoutError()
-        call_id, result = session._exec_mcp_tool(
-            {"call_id": "c1", "mcp_func_name": "send_email", "mcp_args": {}}
-        )
-        assert call_id == "c1"
-        assert "timed out" in result.lower()
-        assert "UNKNOWN" in result
-        assert session._tool_status.get("c1") is EffectStatus.UNKNOWN
-
-    def test_mcp_resource_read_timeout_stays_plain(self):
-        """A resource read is an idempotent read with nothing to reconcile, so
-        its timeout stays a plain failure — no UNKNOWN/reconcile advice and no
-        typed status."""
-        session = _make_session()
-        session._mcp_client = MagicMock()
-        session._mcp_client.read_resource_sync.side_effect = TimeoutError()
-        call_id, result = session._exec_read_resource(
-            {"call_id": "c1", "resource_uri": "file:///doc"}
-        )
-        assert call_id == "c1"
-        assert "timed out" in result.lower()
-        assert "UNKNOWN" not in result
-        assert session._tool_status.get("c1") is None
-
-
-class TestCancelledAgentDisposition:
-    """A cancelled task_agent folds back an honest ledger, not a bare string.
-
-    Regression guard for the HYPOTHESIS.md cancellation appendix: ρ may
-    fabricate the acknowledgment but must not fabricate the outcome —
-    ``unknown``, never ``none``.
-    """
-
-    @staticmethod
-    def _assistant(call_id, name):
-        return Turn.assistant("", tool_calls=(ToolCall(id=call_id, name=name, arguments=""),))
-
-    @staticmethod
-    def _result(call_id, text="ok"):
-        return Turn.tool(call_id, text)
-
-    def test_status_none_when_no_actions(self):
-        """Typed twin of the disposition: a task cancelled before any action is
-        NONE, not UNKNOWN — the complement of the in-flight case."""
-        session = _make_session()
-        assert session._cancelled_agent_status([]) is EffectStatus.NONE
-
-    def test_status_unknown_when_in_flight(self):
-        session = _make_session()
-        msgs = [self._assistant("t1", "bash")]  # issued, no result → in flight
-        assert session._cancelled_agent_status(msgs) is EffectStatus.UNKNOWN
-
-    def test_status_partial_when_all_answered(self):
-        """Every issued call returned but the agent was stopped before finishing
-        — effects are known (not UNKNOWN) yet the task is incomplete: PARTIAL."""
-        session = _make_session()
-        msgs = [self._assistant("t1", "bash"), self._result("t1")]
-        assert session._cancelled_agent_status(msgs) is EffectStatus.PARTIAL
-
-    def test_no_actions_reports_no_side_effects(self, tmp_db):
-        session = _make_session()
-        out = session._cancelled_agent_disposition([], "task")
-        assert "no side effects" in out
-        assert "UNKNOWN" not in out
-
-    def test_marks_in_flight_action_unknown(self, tmp_db):
-        session = _make_session()
-        # bash completed; web_fetch was in flight (issued, no result yet) —
-        # the first unanswered call is the in-flight boundary.
-        msgs = [
-            self._assistant("t1", "bash"),
-            self._result("t1"),
-            self._assistant("t2", "web_fetch"),
-        ]
-        out = session._cancelled_agent_disposition(msgs, "task")
-        assert out != "(task interrupted by user)"
-        assert "Completed before cancel: bash." in out
-        assert "In flight at cancel: web_fetch" in out
-        assert "UNKNOWN" in out
-
-    def test_unanswered_tool_is_in_flight_unknown(self, tmp_db):
-        # An output-flowing bash SIGKILL'd mid-stream raises (no result row) —
-        # it is the in-flight boundary and must read UNKNOWN, never completed.
-        session = _make_session()
-        msgs = [self._assistant("t1", "bash")]  # issued, no result
-        out = session._cancelled_agent_disposition(msgs, "task")
-        assert "In flight at cancel: bash" in out
-        assert "UNKNOWN" in out
-        assert "Completed before cancel" not in out
-
-    def test_all_answered_reports_completed_no_in_flight(self, tmp_db):
-        # Every issued call returned a result — cancel landed between turns,
-        # nothing in flight. Each result carries its own disposition; the
-        # summary just lists what completed, with no UNKNOWN boundary.
-        session = _make_session()
-        msgs = [self._assistant("t1", "bash"), self._result("t1", "(killed)")]
-        out = session._cancelled_agent_disposition(msgs, "task")
-        assert "Completed before cancel: bash." in out
-        assert "In flight at cancel" not in out
-
-    def test_boundary_is_first_unanswered_not_last(self, tmp_db):
-        # Regression (bug-1): a turn issues [bash, web_fetch] executed
-        # sequentially; cancel hits during bash (unanswered, side effects
-        # possible) and web_fetch never runs. The in-flight UNKNOWN must be
-        # bash (the FIRST gap), and web_fetch must read "not started" — NOT
-        # the inverse. The old code took the LAST issued call, labelling the
-        # never-run web_fetch UNKNOWN and the actually-in-flight bash "not
-        # started" — inviting a re-run of the destructive bash.
-        session = _make_session()
-        msgs = [
-            Turn.assistant(
-                "",
-                tool_calls=(
-                    ToolCall(id="t1", name="bash", arguments=""),
-                    ToolCall(id="t2", name="web_fetch", arguments=""),
-                ),
-            )
-        ]  # neither answered: bash raised mid-flight, web_fetch never ran
-        out = session._cancelled_agent_disposition(msgs, "task")
-        assert "In flight at cancel: bash" in out
-        assert "In flight at cancel: web_fetch" not in out
-        assert "Not started (cancelled first): web_fetch." in out
-
-    def test_counts_and_not_started(self, tmp_db):
-        # Turn 1 completes [bash, bash, read_file]; turn 2 issues
-        # [web_fetch (in flight), search (never ran)]. Exercises the ×N
-        # count summary, the first-gap boundary, and not-started.
-        session = _make_session()
-        msgs = [
-            Turn.assistant(
-                "",
-                tool_calls=(
-                    ToolCall(id="t1", name="bash", arguments=""),
-                    ToolCall(id="t2", name="bash", arguments=""),
-                    ToolCall(id="t3", name="read_file", arguments=""),
-                ),
-            ),
-            self._result("t1"),
-            self._result("t2"),
-            self._result("t3"),
-            Turn.assistant(
-                "",
-                tool_calls=(
-                    ToolCall(id="t4", name="web_fetch", arguments=""),
-                    ToolCall(id="t5", name="search", arguments=""),
-                ),
-            ),
-        ]
-        out = session._cancelled_agent_disposition(msgs, "task")
-        assert "Completed before cancel: bash×2, read_file." in out
-        assert "In flight at cancel: web_fetch" in out
-        assert "Not started (cancelled first): search." in out
-
-    def test_exec_task_routes_cancel_to_disposition(self, tmp_db):
-        """_exec_task converts a GenerationCancelled from _run_agent into the
-        honest disposition, reading the in-place-mutated agent_turns."""
-        session = _make_session()
-
-        def fake_run_agent(agent_turns, **kwargs):
-            agent_turns.append(self._assistant("t1", "bash"))
-            agent_turns.append(self._result("t1"))
-            agent_turns.append(self._assistant("t2", "web_fetch"))
-            raise GenerationCancelled()
-
-        with patch.object(session, "_run_agent", side_effect=fake_run_agent):
-            call_id, result = session._exec_task({"call_id": "c1", "prompt": "do x"})
-
-        assert call_id == "c1"
-        assert result != "(task interrupted by user)"
-        assert "UNKNOWN" in result
-        assert "web_fetch" in result  # in-flight boundary
-        assert "bash" in result  # completed
-        # Thread A: the task call's typed status is UNKNOWN (web_fetch in flight).
-        assert session._tool_status.get("c1") is EffectStatus.UNKNOWN
-
-
-class TestEffectStatusPersistence:
-    """Typed effect status rides the role-exclusive ``meta`` column and
-    round-trips through ``reconstruct_turns`` without disturbing the SYSTEM
-    ``source_meta`` that shares the column (no migration; HYPOTHESIS.md
-    effect-record appendix — the ledger persists for audit)."""
-
-    def test_effect_status_meta_envelope(self):
-        assert _effect_status_meta(None) is None
-        assert json.loads(_effect_status_meta(EffectStatus.UNKNOWN)) == {"effect_status": "unknown"}
-
-    def test_reconstruct_routes_tool_effect_status(self):
-        from turnstone.core.storage._utils import reconstruct_turns
-
-        # row: (id, role, content, tool_name, tc_id, provider_data,
-        #       tool_calls, source, event_id, is_error, meta)
-        tool_row = (
-            1,
-            "tool",
-            "timed out. Outcome UNKNOWN ...",
-            None,
-            "call_a",
-            None,
-            None,
-            None,
-            None,
-            True,
-            json.dumps({"effect_status": "unknown"}),
-        )
-        turns = reconstruct_turns([tool_row], "ws1")
-        assert turns[0].effect_status is EffectStatus.UNKNOWN
-        assert turns[0].is_error is True
-
-    def test_reconstruct_leaves_system_source_meta_untouched(self):
-        from turnstone.core.storage._utils import reconstruct_turns
-
-        sys_row = (
-            2,
-            "system",
-            "watch fired",
-            None,
-            None,
-            None,
-            None,
-            "watch_triggered",
-            None,
-            False,
-            json.dumps({"watch_name": "x"}),
-        )
-        turns = reconstruct_turns([sys_row], "ws1")
-        assert turns[0].meta.extra.get("source_meta") == {"watch_name": "x"}
-        assert turns[0].effect_status is None

tests/test_channel_discord.py

@@ -41,11 +41,6 @@ def _bind_ws_event_handlers(bot, cls):
             attr = getattr(cls, name)
             if callable(attr):
                 setattr(bot, name, attr.__get__(bot, cls))
-    # ``_handle_stream_end`` delegates the all-cycles sweep to
-    # ``_pop_ws_approvals``; bind the real method too so dispatcher
-    # tests observe the pop instead of a spec'd AsyncMock no-op.
-    if hasattr(cls, "_pop_ws_approvals"):
-        bot._pop_ws_approvals = cls._pop_ws_approvals.__get__(bot, cls)
 
 
 def _make_message(*, bot=False, guild=True, content="hello", channel=None, reference=None):
@@ -542,7 +537,7 @@ class TestApprovalVerdictDisplay:
                 },
             }
         ]
-        event = ApproveRequestEvent(ws_id="ws-1", cycle_id="cyc-1", items=items)
+        event = ApproveRequestEvent(ws_id="ws-1", items=items)
         _run(bot._on_ws_event("ws-1", thread, event))
 
         # thread.send was called with an embed containing a verdict field
@@ -556,8 +551,8 @@ class TestApprovalVerdictDisplay:
         assert "HIGH" in field.value
         assert "85%" in field.value
 
-        # Pending approval message tracked under (ws_id, cycle_id).
-        assert ("ws-1", "cyc-1") in bot._pending_approval_msgs
+        # Pending approval message tracked
+        assert "ws-1" in bot._pending_approval_msgs
 
     def test_approval_without_verdict(self):
         """ApproveRequestEvent items without verdict still work normally."""
@@ -590,11 +585,10 @@ class TestApprovalVerdictDisplay:
         embed = MagicMock()
         msg.embeds = [embed]
         msg.edit = AsyncMock()
-        bot._pending_approval_msgs[("ws-1", "cyc-1")] = (msg, frozenset({"c-1"}))
+        bot._pending_approval_msgs["ws-1"] = msg
 
         event = IntentVerdictEvent(
             ws_id="ws-1",
-            call_id="c-1",
             func_name="bash",
             risk_level="high",
             recommendation="deny",
@@ -634,10 +628,7 @@ class TestApprovalVerdictDisplay:
         bot._streaming = {}
         bot._thinking_msgs = {}
         bot._tool_info_msgs = {}
-        bot._pending_approval_msgs = {
-            ("ws-1", "cyc-1"): (MagicMock(), frozenset()),
-            ("ws-1", "cyc-2"): (MagicMock(), frozenset()),
-        }
+        bot._pending_approval_msgs = {"ws-1": MagicMock()}
         bot._notify_reply_channels = {}
         _bind_ws_event_handlers(bot, TurnstoneBot)
 
@@ -645,8 +636,7 @@ class TestApprovalVerdictDisplay:
         event = StreamEndEvent(ws_id="ws-1")
         _run(bot._on_ws_event("ws-1", thread, event))
 
-        # ALL of the ws's cycles are swept, not just one entry.
-        assert not bot._pending_approval_msgs
+        assert "ws-1" not in bot._pending_approval_msgs
 
 
 class TestStreamEndBehavior:
@@ -1667,21 +1657,19 @@ class TestApprovalResolved:
         bot = self._make_bot()
         thread = AsyncMock()
 
-        # Set up a pending approval message with components.  The event
-        # below carries no cycle_id (pre-multi-cycle server) — the
-        # legacy fallback clears the ws's single tracked entry.
+        # Set up a pending approval message with components.
         approval_msg = MagicMock()
         approval_msg.embeds = [MagicMock()]
         approval_msg.components = []
         approval_msg.edit = AsyncMock()
-        bot._pending_approval_msgs[("ws-1", "cyc-1")] = (approval_msg, frozenset())
+        bot._pending_approval_msgs["ws-1"] = approval_msg
 
         event = ApprovalResolvedEvent(ws_id="ws-1", approved=False, feedback="timeout")
         _run(bot._on_ws_event("ws-1", thread, event))
 
         approval_msg.edit.assert_awaited_once()
         # Pending approval message should be removed.
-        assert not bot._pending_approval_msgs
+        assert "ws-1" not in bot._pending_approval_msgs
 
     def test_disables_buttons_on_approved(self):
         from turnstone.sdk.events import ApprovalResolvedEvent
@@ -1693,11 +1681,9 @@ class TestApprovalResolved:
         approval_msg.embeds = [MagicMock()]
         approval_msg.components = []
         approval_msg.edit = AsyncMock()
-        bot._pending_approval_msgs[("ws-1", "cyc-1")] = (approval_msg, frozenset())
+        bot._pending_approval_msgs["ws-1"] = approval_msg
 
-        # Cycle-routed resolution: the event's cycle_id selects exactly
-        # this tracked message.
-        event = ApprovalResolvedEvent(ws_id="ws-1", approved=True, cycle_id="cyc-1")
+        event = ApprovalResolvedEvent(ws_id="ws-1", approved=True)
         _run(bot._on_ws_event("ws-1", thread, event))
 
         approval_msg.edit.assert_awaited_once()

tests/test_channel_routing.py

@@ -87,7 +87,7 @@ class TestSendApproval:
         monkeypatch.setattr(router._server, "approve", mock_approve)
         await router.send_approval("ws-1", "corr-abc", approved=True, feedback="ok")
         mock_approve.assert_awaited_once_with(
-            ws_id="ws-1", approved=True, feedback="ok", always=False, cycle_id="corr-abc"
+            ws_id="ws-1", approved=True, feedback="ok", always=False
         )
 
     @pytest.mark.anyio
@@ -99,7 +99,7 @@ class TestSendApproval:
         monkeypatch.setattr(router._server, "approve", mock_approve)
         await router.send_approval("ws-1", "corr-abc", approved=False)
         mock_approve.assert_awaited_once_with(
-            ws_id="ws-1", approved=False, feedback=None, always=False, cycle_id="corr-abc"
+            ws_id="ws-1", approved=False, feedback=None, always=False
         )
 
     @pytest.mark.anyio
@@ -110,9 +110,7 @@ class TestSendApproval:
         mock_approve = AsyncMock()
         monkeypatch.setattr(console_router._console, "route_approve", mock_approve)
         await console_router.send_approval("ws-1", "corr-abc", approved=True, always=True)
-        mock_approve.assert_awaited_once_with(
-            ws_id="ws-1", approved=True, feedback="", always=True, cycle_id="corr-abc"
-        )
+        mock_approve.assert_awaited_once_with(ws_id="ws-1", approved=True, feedback="", always=True)
 
 
 class TestDeleteRoute:

tests/test_channel_slack.py

@@ -576,11 +576,10 @@ class TestApprovalOwnership:
 
         bot, router, client = _make_bot()
         ws_id = "ws-1"
-        bot._pending_approval[(ws_id, "corr-1")] = PendingApproval(  # type: ignore[attr-defined]
+        bot._pending_approval[ws_id] = PendingApproval(  # type: ignore[attr-defined]
             channel="C01SAPU5414",
             message_ts="111.222",
             owner_user_id="U_OWNER",
-            cycle_id="corr-1",
         )
 
         body = {
@@ -599,11 +598,10 @@ class TestApprovalOwnership:
 
         bot, router, client = _make_bot()
         ws_id = "ws-1"
-        bot._pending_approval[(ws_id, "corr-1")] = PendingApproval(  # type: ignore[attr-defined]
+        bot._pending_approval[ws_id] = PendingApproval(  # type: ignore[attr-defined]
             channel="C01SAPU5414",
             message_ts="111.222",
             owner_user_id="U_OWNER",
-            cycle_id="corr-1",
         )
 
         body = {
@@ -622,11 +620,10 @@ class TestApprovalOwnership:
 
         bot, router, client = _make_bot()
         ws_id = "ws-1"
-        bot._pending_approval[(ws_id, "corr-1")] = PendingApproval(  # type: ignore[attr-defined]
+        bot._pending_approval[ws_id] = PendingApproval(  # type: ignore[attr-defined]
             channel="C01SAPU5414",
             message_ts="111.222",
             owner_user_id="U_OWNER",
-            cycle_id="corr-1",
         )
 
         body = {
@@ -779,9 +776,7 @@ class TestWsEventDispatch:
         bot, client = self._make_ws_bot()
 
         event = ApproveRequestEvent(
-            ws_id="ws-1",
-            cycle_id="cyc-1",
-            items=[{"call_id": "c-1", "func_name": "bash", "needs_approval": True}],
+            ws_id="ws-1", items=[{"func_name": "bash", "needs_approval": True}]
         )
         route = SlackRoute(channel="C1", user_id="U12345", thread_ts="123.456")
         _run(bot._on_ws_event("ws-1", route, event))  # type: ignore[attr-defined]
@@ -789,12 +784,8 @@ class TestWsEventDispatch:
         client.chat_postMessage.assert_awaited_once()
         call_kwargs = client.chat_postMessage.call_args[1]
         assert "blocks" in call_kwargs
-        # Tracked under (ws_id, cycle_id) so concurrent cycles each get
-        # their own Slack message.
-        entry = bot._pending_approval[("ws-1", "cyc-1")]  # type: ignore[attr-defined]
-        assert entry.owner_user_id == "U12345"
-        assert entry.cycle_id == "cyc-1"
-        assert entry.call_ids == frozenset({"c-1"})
+        assert "ws-1" in bot._pending_approval  # type: ignore[attr-defined]
+        assert bot._pending_approval["ws-1"].owner_user_id == "U12345"  # type: ignore[attr-defined]
 
     def test_intent_verdict_updates_approval_message(self) -> None:
         from turnstone.channels.slack.bot import PendingApproval
@@ -806,17 +797,14 @@ class TestWsEventDispatch:
             return_value={"ok": True, "messages": [{"blocks": []}]}
         )
 
-        bot._pending_approval[("ws-1", "cyc-1")] = PendingApproval(  # type: ignore[attr-defined]
+        bot._pending_approval["ws-1"] = PendingApproval(  # type: ignore[attr-defined]
             channel="C1",
             message_ts="999.000",
             owner_user_id="U12345",
-            cycle_id="cyc-1",
-            call_ids=frozenset({"c-1"}),
         )
 
         event = IntentVerdictEvent(
             ws_id="ws-1",
-            call_id="c-1",
             func_name="bash",
             risk_level="high",
             confidence=0.9,
@@ -833,20 +821,17 @@ class TestWsEventDispatch:
         from turnstone.sdk.events import ApprovalResolvedEvent
 
         bot, client = self._make_ws_bot()
-        bot._pending_approval[("ws-1", "cyc-9")] = PendingApproval(  # type: ignore[attr-defined]
+        bot._pending_approval["ws-1"] = PendingApproval(  # type: ignore[attr-defined]
             channel="C1",
             message_ts="999.000",
             owner_user_id="U12345",
-            cycle_id="cyc-9",
         )
 
-        # Event WITHOUT a cycle_id (pre-multi-cycle server): the legacy
-        # fallback clears the ws's single tracked entry, as before.
         event = ApprovalResolvedEvent(ws_id="ws-1", approved=True)
         route = SlackRoute(channel="C1", user_id="U12345", thread_ts="123.456")
         _run(bot._on_ws_event("ws-1", route, event))  # type: ignore[attr-defined]
 
-        assert not bot._pending_approval  # type: ignore[attr-defined]
+        assert "ws-1" not in bot._pending_approval  # type: ignore[attr-defined]
         client.chat_update.assert_awaited_once()
 
     def test_link_prefix_does_not_hijack_regular_prompt(self) -> None:

tests/test_compaction_checkpoint.py

@@ -1,449 +0,0 @@
-"""Tests for persisted compaction checkpoints (rehydration-deadlock fix).
-
-Compaction swaps a session's in-memory history for a summary but leaves the full
-transcript in storage.  Without a durable marker, ``resume()`` reloaded the full
-pre-compaction history, which on a long session — or one switched to a smaller-
-context model — exceeds the window and deadlocks the first send.
-
-The fix persists one ``_source="compaction"`` marker (summary + watermark) so
-resume rehydrates ``[summary] + [rows after the watermark]`` while the full
-history stays in storage for ``/history``/export.  Covered here:
-
-- ``get_compaction_watermark`` — the boundary id (max-summarized), with and
-  without a preserved tail, and on an empty workstream.
-- ``load_message_turns`` (resume) — checkpoint-aware slice, latest-marker-wins,
-  preserved-tail handling, and the full-history fallbacks (no marker, malformed
-  marker) that keep every pre-checkpoint session loading exactly as before.
-- ``load_messages`` (display) — markers stay invisible to ``/history``.
-- End-to-end: ``_compact_messages`` writes the marker and a fresh ``resume()``
-  rehydrates the bounded view, not the full transcript.
-"""
-
-from __future__ import annotations
-
-import json
-
-import pytest
-
-from tests._session_helpers import make_session
-from turnstone.core.trajectory import turns_from_dicts
-
-
-def _marker_meta(watermark: int | None) -> str | None:
-    """The marker's stored ``meta`` JSON (``None`` simulates a legacy/malformed marker)."""
-    return json.dumps({"watermark": watermark}) if watermark is not None else None
-
-
-def _register(st, ws: str = "ws1") -> str:
-    st.register_workstream(ws, user_id="u1", title="t", kind="interactive")
-    return ws
-
-
-# ---------------------------------------------------------------------------
-# get_compaction_watermark
-# ---------------------------------------------------------------------------
-
-
-class TestWatermark:
-    def test_preserve_tail_zero_is_max_id(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        ids = [st.save_message(ws, "user", f"m{i}") for i in range(5)]
-        assert st.get_compaction_watermark(ws, 0) == max(ids)
-
-    def test_preserve_tail_n_is_nth_newest(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        ids = sorted(st.save_message(ws, "user", f"m{i}") for i in range(5))
-        # Keep the newest 2 verbatim → boundary is the 3rd-newest id.
-        assert st.get_compaction_watermark(ws, 2) == ids[-3]
-
-    def test_preserve_tail_ignores_existing_markers(self, storage_backend):
-        # A compaction marker is saved as a NEW row but is not part of the
-        # preserved in-memory tail, so it must not shift the (preserve_tail+1)
-        # boundary — without the exclusion, this returns ids[-1] (the marker
-        # consumes an offset slot) and resume would drop a real tail row.
-        st = storage_backend
-        ws = _register(st)
-        ids = [st.save_message(ws, "user", f"m{i}") for i in range(5)]
-        st.save_message(ws, "assistant", "SUM", source="compaction", meta=_marker_meta(max(ids)))
-        st.save_message(ws, "user", "m5")
-        # Real rows newest-first: m5, m4, m3, ... → 3rd-newest real row is m3.
-        assert st.get_compaction_watermark(ws, 2) == ids[-2]
-
-    def test_empty_workstream_is_none(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        assert st.get_compaction_watermark(ws, 0) is None
-
-    def test_preserve_tail_exceeding_row_count_is_none(self, storage_backend):
-        # Fewer rows than the preserved tail → no boundary, so compaction skips
-        # the marker rather than writing a watermark that points past the history.
-        st = storage_backend
-        ws = _register(st)
-        st.save_message(ws, "user", "only")
-        assert st.get_compaction_watermark(ws, 5) is None
-
-
-# ---------------------------------------------------------------------------
-# load_message_turns — checkpoint-aware resume
-# ---------------------------------------------------------------------------
-
-
-class TestCheckpointResume:
-    def test_loads_summary_plus_tail_not_full_history(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        for i in range(5):
-            st.save_message(ws, "user" if i % 2 == 0 else "assistant", f"old{i}")
-        watermark = st.get_compaction_watermark(ws, 0)
-        st.save_message(
-            ws, "assistant", "THE SUMMARY", source="compaction", meta=_marker_meta(watermark)
-        )
-        st.save_message(ws, "user", "new question")
-        st.save_message(ws, "assistant", "new answer")
-
-        texts = [t.text for t in st.load_message_turns(ws)]
-        assert texts == ["[Conversation summary]", "THE SUMMARY", "new question", "new answer"]
-        assert not any("old" in x for x in texts)  # summarized prefix is gone
-
-    def test_preserved_tail_kept_after_summary(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        ids = sorted(st.save_message(ws, "user", f"m{i}") for i in range(4))
-        # Mid-turn compaction keeps the newest row (m3) verbatim.
-        watermark = st.get_compaction_watermark(ws, 1)
-        assert watermark == ids[-2]
-        st.save_message(ws, "assistant", "SUM", source="compaction", meta=_marker_meta(watermark))
-
-        texts = [t.text for t in st.load_message_turns(ws)]
-        assert texts == ["[Conversation summary]", "SUM", "m3"]
-
-    def test_latest_marker_wins(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        st.save_message(ws, "user", "old")
-        st.save_message(
-            ws,
-            "assistant",
-            "SUMMARY 1",
-            source="compaction",
-            meta=_marker_meta(st.get_compaction_watermark(ws, 0)),
-        )
-        st.save_message(ws, "user", "mid")
-        st.save_message(
-            ws,
-            "assistant",
-            "SUMMARY 2",
-            source="compaction",
-            meta=_marker_meta(st.get_compaction_watermark(ws, 0)),
-        )
-        st.save_message(ws, "user", "after")
-
-        texts = [t.text for t in st.load_message_turns(ws)]
-        assert texts == ["[Conversation summary]", "SUMMARY 2", "after"]
-        assert "SUMMARY 1" not in texts and "old" not in texts and "mid" not in texts
-
-    def test_no_marker_loads_full_history(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        for i in range(3):
-            st.save_message(ws, "user", f"m{i}")
-        assert [t.text for t in st.load_message_turns(ws)] == ["m0", "m1", "m2"]
-
-    def test_malformed_marker_falls_back_to_full_history(self, storage_backend):
-        # A marker with no watermark (legacy/corrupt) must NOT slice — losing
-        # real messages is worse than reloading more than necessary.
-        st = storage_backend
-        ws = _register(st)
-        st.save_message(ws, "user", "a")
-        st.save_message(ws, "assistant", "SUMMARY", source="compaction", meta=None)
-        st.save_message(ws, "user", "b")
-        texts = [t.text for t in st.load_message_turns(ws)]
-        assert "a" in texts and "b" in texts  # no real message dropped
-
-
-# ---------------------------------------------------------------------------
-# load_messages — display path keeps markers invisible
-# ---------------------------------------------------------------------------
-
-
-class TestDisplayPath:
-    def test_history_excludes_marker(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        st.save_message(ws, "user", "q")
-        st.save_message(ws, "assistant", "a")
-        st.save_message(
-            ws,
-            "assistant",
-            "SUMMARY",
-            source="compaction",
-            meta=_marker_meta(st.get_compaction_watermark(ws, 0)),
-        )
-
-        contents = [m.get("content") for m in st.load_messages(ws)]
-        assert "SUMMARY" not in contents
-        assert contents == ["q", "a"]  # true transcript, no injected summary
-
-
-# ---------------------------------------------------------------------------
-# End-to-end: compaction writes the marker, resume is bounded
-# ---------------------------------------------------------------------------
-
-
-def test_compaction_persists_checkpoint_and_resume_is_bounded(tmp_db, mock_openai_client):
-    """The deadlock-fix proof: a session compacts, a fresh session reopens it,
-    and resume rehydrates [summary]+[tail] — never the full pre-compaction
-    transcript that would overflow the window on reopen."""
-    from unittest.mock import patch
-
-    from turnstone.core.memory import register_workstream, save_message
-
-    ws = "wsE2E"
-    register_workstream(ws, user_id="u1", name="t")
-    history = [
-        {"role": "user" if i % 2 == 0 else "assistant", "content": f"turn {i}"} for i in range(6)
-    ]
-    for h in history:
-        save_message(ws, h["role"], h["content"])
-
-    sess = make_session(client=mock_openai_client, context_window=10_000, max_tokens=1_000)
-    sess._ws_id = ws
-    sess.messages = turns_from_dicts(history)
-    sess._msg_tokens = [1] * len(history)
-    with patch.object(sess, "_summarize_blocks", return_value="DENSE SUMMARY"):
-        assert sess._compact_messages(auto=False) is True
-
-    # Conversation continues after the compaction.
-    save_message(ws, "user", "after compaction")
-
-    # A fresh session reopens the workstream.
-    sess2 = make_session(client=mock_openai_client, context_window=10_000, max_tokens=1_000)
-    assert sess2.resume(ws) is True
-    texts = [t.text for t in sess2.messages]
-
-    assert texts[:2] == ["[Conversation summary]", "DENSE SUMMARY"]
-    assert "after compaction" in texts
-    assert not any(t.startswith("turn ") for t in texts)  # full history NOT reloaded
-
-
-# ---------------------------------------------------------------------------
-# Malformed / edge-case markers — the watermark guards and the empty tail
-# ---------------------------------------------------------------------------
-
-
-class TestMarkerEdges:
-    @pytest.mark.parametrize(
-        "meta",
-        [
-            json.dumps({"watermark": "5"}),  # non-int (string)
-            json.dumps({"watermark": True}),  # bool — True is an int subclass
-            json.dumps({}),  # key absent
-            json.dumps({"watermark": None}),  # null
-        ],
-    )
-    def test_non_int_watermark_falls_back_to_full_history(self, storage_backend, meta):
-        # A watermark that isn't a real int must NOT slice (a True watermark
-        # would otherwise cut at id 1 and drop real history).
-        st = storage_backend
-        ws = _register(st)
-        st.save_message(ws, "user", "a")
-        st.save_message(ws, "assistant", "b")
-        st.save_message(ws, "assistant", "SUMMARY", source="compaction", meta=meta)
-        st.save_message(ws, "user", "c")
-        texts = [t.text for t in st.load_message_turns(ws)]
-        assert "a" in texts and "b" in texts and "c" in texts  # nothing sliced away
-        # ...and the malformed marker is DROPPED, not leaked as a stray summary turn.
-        assert "SUMMARY" not in texts
-
-    def test_marker_as_final_row_yields_empty_tail(self, storage_backend):
-        # watermark == max id, marker is the last row → resume is just the summary.
-        st = storage_backend
-        ws = _register(st)
-        for i in range(3):
-            st.save_message(ws, "user", f"old{i}")
-        wm = st.get_compaction_watermark(ws, 0)
-        st.save_message(ws, "assistant", "SUMMARY", source="compaction", meta=_marker_meta(wm))
-        assert [t.text for t in st.load_message_turns(ws)] == ["[Conversation summary]", "SUMMARY"]
-
-
-# ---------------------------------------------------------------------------
-# checkpointed=False — export/audit gets the FULL transcript (markers dropped)
-# ---------------------------------------------------------------------------
-
-
-class TestFullHistoryLoad:
-    def test_checkpointed_false_returns_full_history_without_marker(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        for i in range(4):
-            st.save_message(ws, "user" if i % 2 == 0 else "assistant", f"old{i}")
-        wm = st.get_compaction_watermark(ws, 0)
-        st.save_message(ws, "assistant", "SUMMARY", source="compaction", meta=_marker_meta(wm))
-        st.save_message(ws, "user", "after")
-
-        # Resume (default) is bounded; export (checkpointed=False) is full + marker-free.
-        assert [t.text for t in st.load_message_turns(ws)] == [
-            "[Conversation summary]",
-            "SUMMARY",
-            "after",
-        ]
-        full = [t.text for t in st.load_message_turns(ws, checkpointed=False)]
-        assert full == ["old0", "old1", "old2", "old3", "after"]
-        assert "SUMMARY" not in full and "[Conversation summary]" not in full
-
-
-# ---------------------------------------------------------------------------
-# search — compaction markers stay out of search results
-# ---------------------------------------------------------------------------
-
-
-class TestSearchExclusion:
-    def test_search_history_excludes_markers(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        st.save_message(ws, "user", "findme apple")
-        st.save_message(
-            ws,
-            "assistant",
-            "findme SUMMARY banana",
-            source="compaction",
-            meta=_marker_meta(st.get_compaction_watermark(ws, 0)),
-        )
-        contents = [r[3] for r in st.search_history("findme")]
-        assert any("apple" in (c or "") for c in contents)  # real row matched
-        assert not any("SUMMARY" in (c or "") for c in contents)  # marker excluded
-        # ...and normal rows (whose _source is NULL) are NOT dropped by the filter.
-        assert contents
-
-    def test_search_history_recent_excludes_markers(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        st.save_message(ws, "user", "real")
-        st.save_message(
-            ws,
-            "assistant",
-            "SUMMARY",
-            source="compaction",
-            meta=_marker_meta(st.get_compaction_watermark(ws, 0)),
-        )
-        recent = [r[3] for r in st.search_history_recent(10)]
-        assert "real" in recent and "SUMMARY" not in recent
-
-
-# ---------------------------------------------------------------------------
-# rewind / retry — compaction-safe truncation (never delete the summary backing)
-# ---------------------------------------------------------------------------
-
-
-class TestCompactionFloor:
-    def test_floor_and_count(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        for i in range(3):
-            st.save_message(ws, "user", f"old{i}")  # summarized prefix
-        wm = st.get_compaction_watermark(ws, 0)
-        st.save_message(ws, "assistant", "SUMMARY", source="compaction", meta=_marker_meta(wm))
-        st.save_message(ws, "user", "tail1")
-        st.save_message(ws, "assistant", "tail2")
-        assert st.get_compaction_floor(ws) == 4  # 3 prefix + 1 marker
-        assert st.count_messages(ws) == 6
-
-    def test_floor_zero_without_marker(self, storage_backend):
-        st = storage_backend
-        ws = _register(st)
-        st.save_message(ws, "user", "x")
-        assert st.get_compaction_floor(ws) == 0
-
-
-def test_rewind_after_compaction_never_deletes_summary_backing(tmp_db, mock_openai_client):
-    """The review's major rewind finding: after a compaction, a tail-trim must
-    delete from the storage TAIL and floor at the marker, not keep the oldest
-    summarized rows and drop the marker."""
-    from turnstone.core.memory import get_storage, register_workstream, save_message
-
-    ws = "wsRW"
-    register_workstream(ws, user_id="u1", name="t")
-    for i in range(3):
-        save_message(ws, "user", f"old{i}")  # prefix
-    st = get_storage()
-    wm = st.get_compaction_watermark(ws, 0)
-    save_message(
-        ws, "assistant", "SUMMARY", source="compaction", meta=json.dumps({"watermark": wm})
-    )
-    save_message(ws, "user", "q1")  # tail
-    save_message(ws, "assistant", "a1")  # tail
-    assert st.get_compaction_floor(ws) == 4 and st.count_messages(ws) == 6
-
-    sess = make_session(client=mock_openai_client, context_window=10_000, max_tokens=1_000)
-    sess._ws_id = ws
-
-    # Trim one tail turn → keep = max(floor 4, total 6 - 1) = 5 → deletes only "a1".
-    sess._persist_truncation(1)
-    assert st.count_messages(ws) == 5
-    survived = [t.text for t in st.load_message_turns(ws)]
-    assert survived[:2] == ["[Conversation summary]", "SUMMARY"]  # marker + prefix intact
-    assert "q1" in survived
-
-    # Over-deep trim → clamps at the floor; the marker + prefix still survive.
-    sess._persist_truncation(100)
-    assert st.count_messages(ws) == 4  # floored at prefix + marker
-    after = [t.text for t in st.load_message_turns(ws)]
-    assert after == ["[Conversation summary]", "SUMMARY"]  # summary backing never deleted
-
-
-def test_persist_truncation_uncompacted_matches_plain_tail_delete(tmp_db, mock_openai_client):
-    """With no compaction (floor 0), the new path is identical to the old
-    keep=len(self.messages) tail delete."""
-    from turnstone.core.memory import get_storage, register_workstream, save_message
-
-    ws = "wsPlain"
-    register_workstream(ws, user_id="u1", name="t")
-    for i in range(5):
-        save_message(ws, "user", f"m{i}")
-    st = get_storage()
-    assert st.get_compaction_floor(ws) == 0
-
-    sess = make_session(client=mock_openai_client, context_window=10_000, max_tokens=1_000)
-    sess._ws_id = ws
-    sess._persist_truncation(2)  # remove the last 2
-    assert st.count_messages(ws) == 3
-
-
-def test_persist_truncation_skips_delete_when_count_unavailable(tmp_db, mock_openai_client):
-    """count_messages==0 (the storage-error sentinel) must NOT delete — a wrong
-    truncation would lose user history."""
-    from unittest.mock import patch
-
-    from turnstone.core.memory import get_storage, register_workstream, save_message
-
-    ws = "wsCnt"
-    register_workstream(ws, user_id="u1", name="t")
-    for i in range(4):
-        save_message(ws, "user", f"m{i}")
-    st = get_storage()
-    sess = make_session(client=mock_openai_client)
-    sess._ws_id = ws
-    with patch("turnstone.core.session.count_messages", return_value=0):
-        sess._persist_truncation(2)
-    assert st.count_messages(ws) == 4  # nothing deleted
-
-
-def test_persist_truncation_skips_delete_when_floor_unavailable(tmp_db, mock_openai_client):
-    """get_compaction_floor==-1 (the storage-error sentinel) must NOT delete — a 0
-    floor on a compacted ws could otherwise drop the marker on an over-deep trim."""
-    from unittest.mock import patch
-
-    from turnstone.core.memory import get_storage, register_workstream, save_message
-
-    ws = "wsFloor"
-    register_workstream(ws, user_id="u1", name="t")
-    for i in range(4):
-        save_message(ws, "user", f"m{i}")
-    st = get_storage()
-    sess = make_session(client=mock_openai_client)
-    sess._ws_id = ws
-    with patch("turnstone.core.session.get_compaction_floor", return_value=-1):
-        sess._persist_truncation(2)
-    assert st.count_messages(ws) == 4  # nothing deleted

tests/test_compaction_crossing.py

@@ -1,383 +0,0 @@
-"""Tests for the compaction crossing discipline: what crosses the summary
-boundary VERBATIM (not only as summarizer paraphrase) and how the synthetic
-summary turns are recognized.
-
-- **Provenance tags** — ``_compact_messages`` and
-  ``reconstruct_turns_checkpointed`` mark both synthetic summary turns
-  ``source="compaction"``; ``_find_turn_boundaries`` and ``_generate_title``
-  test the tag, not the ``[Conversation summary]`` content string.  A user
-  who literally types the label therefore stays a REAL turn (previously it
-  was silently treated as synthetic — provenance by spelling).
-- **Carry budget** — ``_carry_budget_chars`` scales the verbatim-carry
-  allowance to ~25% of the window (clamped by the summary output reserve,
-  floored at ``_MIN_CARRY_BUDGET_CHARS``), replacing the fixed 400-char
-  continuation-hint clip; oversize content keeps head + tail around an
-  honest marker.
-- **Wind-down spill** — with ``carry_spill=True`` (the end-of-turn site
-  passes the ``stopped_to_compact`` latch) the final summarized assistant
-  turn's text is copied onto the summary under ``## Wind-down (verbatim)``
-  — shell concatenation, so the model's own plan statement survives the
-  collapse even when the summarizer paraphrases it.
-- The overflow-backstop compact-and-retry passes ``my_generation`` so a
-  stale send cannot compact-and-swap a newer generation's history.
-"""
-
-from __future__ import annotations
-
-import json
-from types import SimpleNamespace
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from tests._session_helpers import make_session
-from turnstone.core.session import COMPACTION_SOURCE, COMPACTION_SUMMARY_LABEL
-from turnstone.core.trajectory import turns_from_dicts
-
-
-@pytest.fixture
-def session(tmp_db, mock_openai_client):
-    """Small-window session: context_window=10_000, compact_max_tokens=100 so
-    the summary output reserve is tiny and the carry budget is easy to compute
-    (reserve=100, margin=500, spare=9_400, budget=min(2_500, 9_400)=2_500
-    tokens → 10_000 chars at the uncalibrated 4.0 chars/token)."""
-    return make_session(
-        client=mock_openai_client,
-        context_window=10_000,
-        compact_max_tokens=100,
-        max_tokens=1_000,
-        tool_timeout=10,
-    )
-
-
-def _stub_summary(text: str = "DENSE"):
-    return SimpleNamespace(content=text, finish_reason="stop")
-
-
-# ---------------------------------------------------------------------------
-# Provenance tags on the synthetic summary turns
-# ---------------------------------------------------------------------------
-
-
-class TestSummaryTurnProvenance:
-    def test_compact_tags_both_summary_turns(self, session):
-        session.messages = turns_from_dicts(
-            [
-                {"role": "user", "content": "do the thing"},
-                {"role": "assistant", "content": "did the thing"},
-            ]
-        )
-        session._msg_tokens = [1, 1]
-        with patch.object(session, "_utility_completion", return_value=_stub_summary()):
-            assert session._compact_messages(auto=True) is True
-
-        label, summary = session.messages[0], session.messages[1]
-        assert label.text == COMPACTION_SUMMARY_LABEL
-        assert label.source == COMPACTION_SOURCE
-        assert summary.source == COMPACTION_SOURCE
-
-    def test_boundaries_exclude_tagged_label_only(self, session):
-        session.messages = turns_from_dicts(
-            [
-                {
-                    "role": "user",
-                    "content": COMPACTION_SUMMARY_LABEL,
-                    "_source": COMPACTION_SOURCE,
-                },
-                {"role": "assistant", "content": "summary"},
-                {"role": "user", "content": "real follow-up"},
-            ]
-        )
-        assert session._find_turn_boundaries() == [2]
-
-    def test_literal_label_from_user_is_a_real_boundary(self, session):
-        """A user who literally types '[Conversation summary]' is not a
-        compaction artifact — provenance rides the tag, not the spelling."""
-        session.messages = turns_from_dicts([{"role": "user", "content": COMPACTION_SUMMARY_LABEL}])
-        assert session._find_turn_boundaries() == [0]
-
-    def test_title_gen_titles_from_literal_label_user(self, session):
-        """The tag distinction reaches _generate_title: a synthetic label is
-        skipped (pinned in test_cooperative_compaction), but a REAL user
-        message that happens to equal the label is titled from normally."""
-        session.messages = turns_from_dicts(
-            [
-                {"role": "user", "content": COMPACTION_SUMMARY_LABEL},
-                {"role": "assistant", "content": "an answer"},
-            ]
-        )
-        with (
-            patch.object(
-                session, "_utility_completion", return_value=_stub_summary("A Title")
-            ) as uc,
-            patch.object(session, "ui", new=MagicMock()),
-        ):
-            session._generate_title()
-
-        uc.assert_called_once()
-        prompt = uc.call_args[0][0][-1]["content"]
-        assert COMPACTION_SUMMARY_LABEL in prompt  # titled FROM the real message
-
-
-class TestCheckpointReconstructionProvenance:
-    def test_resume_turns_carry_compaction_source(self, storage_backend):
-        """A reopened session must see the same provenance the live session
-        held: reconstruct_turns_checkpointed tags the synthetic label AND the
-        marker-backed summary turn, while real tail rows stay untagged."""
-        st = storage_backend
-        st.register_workstream("ws1", user_id="u1", title="t", kind="interactive")
-        st.save_message("ws1", "user", "old question")
-        st.save_message("ws1", "assistant", "old answer")
-        watermark = st.get_compaction_watermark("ws1", 0)
-        st.save_message(
-            "ws1",
-            "assistant",
-            "THE SUMMARY",
-            source=COMPACTION_SOURCE,
-            meta=json.dumps({"watermark": watermark}),
-        )
-        st.save_message("ws1", "user", "new question")
-
-        turns = st.load_message_turns("ws1")
-        assert [t.text for t in turns] == [
-            COMPACTION_SUMMARY_LABEL,
-            "THE SUMMARY",
-            "new question",
-        ]
-        assert turns[0].source == COMPACTION_SOURCE
-        assert turns[1].source == COMPACTION_SOURCE
-        assert turns[2].source is None
-
-
-# ---------------------------------------------------------------------------
-# Carry budget — the verbatim-crossing allowance
-# ---------------------------------------------------------------------------
-
-
-def _isolate_overhead(s, system_tokens: int = 0) -> None:
-    """Pin the fixed prompt overhead (system + tool defs) for exact budget
-    arithmetic — the real values vary with the composed prompt and registered
-    tools (same isolation pattern as TestRemainingTokenBudget)."""
-    s._system_tokens = system_tokens
-    s._tools = []
-
-
-class TestCarryBudget:
-    def test_scales_to_quarter_window(self, session):
-        # overhead=0, reserve=100 (compact_max_tokens), margin=500,
-        # spare=9_400; min(10_000 // 4, 9_400) = 2_500 tokens * 4.0 chars/token.
-        _isolate_overhead(session)
-        assert session._carry_budget_chars() == 10_000
-
-    def test_floors_on_tiny_window(self, tmp_db, mock_openai_client):
-        tiny = make_session(client=mock_openai_client, context_window=1_000, tool_timeout=10)
-        _isolate_overhead(tiny)
-        assert tiny._carry_budget_chars() == tiny._MIN_CARRY_BUDGET_CHARS
-
-    @pytest.mark.parametrize("carries", [1, 2])
-    def test_overhead_reserve_and_carries_fit_window_at_shipped_defaults(
-        self, tmp_db, mock_openai_client, carries
-    ):
-        """The invariant that prevents a carry-induced overflow, pinned at the
-        SHIPPED defaults (budget bugs hide behind test-sized configs), for
-        BOTH carry counts, and INCLUDING the fixed prompt overhead: the
-        post-compaction prompt is system + tools + summary + carries, so a
-        budget that ignores the overhead (or sizes carries independently)
-        stacks past the window and the backstop re-compacts the carries
-        away."""
-        s = make_session(client=mock_openai_client, tool_timeout=10)
-        _isolate_overhead(s, system_tokens=4_000)  # a chunky composed prompt
-        reserve = s._summary_output_tokens()
-        per_carry_tokens = s._carry_budget_chars(carries) / s._chars_per_token
-        margin = int(s.context_window * s._SUMMARY_SAFETY_MARGIN)
-        assert 4_000 + reserve + carries * per_carry_tokens + margin <= s.context_window
-
-    def test_budget_shrinks_with_prompt_overhead(self, tmp_db, mock_openai_client):
-        """Monotonicity pin: the overhead term is genuinely in the formula —
-        a bigger system prompt leaves less to carry."""
-        s = make_session(client=mock_openai_client, tool_timeout=10)
-        _isolate_overhead(s, system_tokens=0)
-        roomy = s._carry_budget_chars(2)
-        _isolate_overhead(s, system_tokens=8_000)
-        assert s._carry_budget_chars(2) < roomy
-
-    def test_double_carry_splits_the_spare(self, tmp_db, mock_openai_client):
-        """At shipped defaults the spare (window − overhead − reserve −
-        margin) binds two carries: each gets spare // 2, strictly less than
-        the solo quarter-window allowance."""
-        s = make_session(client=mock_openai_client, tool_timeout=10)
-        _isolate_overhead(s, system_tokens=2_000)
-        reserve = s._summary_output_tokens()
-        margin = int(s.context_window * s._SUMMARY_SAFETY_MARGIN)
-        spare = s.context_window - reserve - margin - 2_000
-        assert s._carry_budget_chars(2) == int((spare // 2) * s._chars_per_token)
-        assert s._carry_budget_chars(2) < s._carry_budget_chars(1)
-
-
-class TestContinuationHintCarry:
-    def test_long_ask_crosses_verbatim(self, session):
-        """A 3_000-char user message is within the 10_000-char carry budget and
-        must cross whole — the old fixed clip kept 400 chars of it."""
-        ask = "spec line\n" * 300  # 3_000 chars
-        session.messages = turns_from_dicts(
-            [
-                {"role": "user", "content": ask},
-                {"role": "assistant", "content": "working on it"},
-            ]
-        )
-        session._msg_tokens = [1, 1]
-        with patch.object(session, "_utility_completion", return_value=_stub_summary()):
-            assert session._compact_messages(auto=True) is True
-
-        summary_text = session.messages[1].text or ""
-        assert ask.strip() in summary_text  # verbatim, not clipped
-        assert "## Continue" in summary_text
-
-    def test_oversize_ask_keeps_head_and_tail_with_marker(self, session):
-        head_sentinel = "HEAD-OF-SPEC"
-        tail_sentinel = "TAIL-OF-SPEC"
-        ask = head_sentinel + ("x" * 20_000) + tail_sentinel  # over the 10_000 budget
-        session.messages = turns_from_dicts(
-            [
-                {"role": "user", "content": ask},
-                {"role": "assistant", "content": "working on it"},
-            ]
-        )
-        session._msg_tokens = [1, 1]
-        with patch.object(session, "_utility_completion", return_value=_stub_summary()):
-            assert session._compact_messages(auto=True) is True
-
-        summary_text = session.messages[1].text or ""
-        assert head_sentinel in summary_text
-        assert tail_sentinel in summary_text
-        # The marker reports the ORIGINAL size, and the summary tells the
-        # model the full text is retrievable — a truncated carry is a cache
-        # miss with a pointer, not a silent loss.
-        assert f"…[truncated — {len(ask):,} chars total]…" in summary_text
-        assert "the recall tool can retrieve it" in summary_text
-        assert ask not in summary_text  # genuinely truncated
-
-    def test_untruncated_carry_gets_no_recall_pointer(self, session):
-        """The retrievability note appears ONLY when something was cut."""
-        session.messages = turns_from_dicts(
-            [
-                {"role": "user", "content": "short ask"},
-                {"role": "assistant", "content": "working on it"},
-            ]
-        )
-        session._msg_tokens = [1, 1]
-        with patch.object(session, "_utility_completion", return_value=_stub_summary()):
-            assert session._compact_messages(auto=True) is True
-        assert "recall tool" not in (session.messages[1].text or "")
-
-
-# ---------------------------------------------------------------------------
-# Wind-down spill — the model's plan statement crosses verbatim
-# ---------------------------------------------------------------------------
-
-
-class TestWindDownSpill:
-    SPILL = (
-        "Goal: finish the migration.\n"
-        "Remaining: backfill rows 300-900, rerun the verifier.\n"
-        "Next step: resume at scripts/backfill.py --from 300."
-    )
-
-    def _compacted_summary(self, session, *, carry_spill: bool) -> str:
-        session.messages = turns_from_dicts(
-            [
-                {"role": "user", "content": "please migrate the database"},
-                {"role": "assistant", "content": self.SPILL},
-            ]
-        )
-        session._msg_tokens = [1, 1]
-        with patch.object(session, "_utility_completion", return_value=_stub_summary()):
-            assert session._compact_messages(auto=True, carry_spill=carry_spill) is True
-        return session.messages[1].text or ""
-
-    def test_spill_copied_verbatim_under_heading(self, session):
-        summary_text = self._compacted_summary(session, carry_spill=True)
-        assert "## Wind-down (verbatim)" in summary_text
-        assert self.SPILL in summary_text  # copied, not paraphrased
-        # Ordering: recorded plan first, then how to resume.
-        assert summary_text.index("## Wind-down (verbatim)") < summary_text.index("## Continue")
-
-    def test_no_spill_without_flag(self, session):
-        summary_text = self._compacted_summary(session, carry_spill=False)
-        assert "## Wind-down (verbatim)" not in summary_text
-
-    def test_no_spill_when_last_summarized_turn_is_not_assistant(self, session):
-        session.messages = turns_from_dicts(
-            [
-                {"role": "assistant", "content": "answer"},
-                {"role": "user", "content": "next task"},
-            ]
-        )
-        session._msg_tokens = [1, 1]
-        with patch.object(session, "_utility_completion", return_value=_stub_summary()):
-            assert session._compact_messages(auto=True, carry_spill=True) is True
-        assert "## Wind-down (verbatim)" not in (session.messages[1].text or "")
-
-    def test_empty_spill_adds_no_heading(self, session):
-        session.messages = turns_from_dicts(
-            [
-                {"role": "user", "content": "task"},
-                {"role": "assistant", "content": "   "},
-            ]
-        )
-        session._msg_tokens = [1, 1]
-        with patch.object(session, "_utility_completion", return_value=_stub_summary()):
-            assert session._compact_messages(auto=True, carry_spill=True) is True
-        assert "## Wind-down (verbatim)" not in (session.messages[1].text or "")
-
-    def test_oversize_spill_truncated_by_carry_budget(self, session):
-        big_spill = "PLAN-HEAD " + ("y" * 20_000) + " PLAN-TAIL"
-        session.messages = turns_from_dicts(
-            [
-                {"role": "user", "content": "task"},
-                {"role": "assistant", "content": big_spill},
-            ]
-        )
-        session._msg_tokens = [1, 1]
-        with patch.object(session, "_utility_completion", return_value=_stub_summary()):
-            assert session._compact_messages(auto=True, carry_spill=True) is True
-        summary_text = session.messages[1].text or ""
-        assert "PLAN-HEAD" in summary_text and "PLAN-TAIL" in summary_text
-        assert "…[truncated —" in summary_text
-        assert "the recall tool can retrieve it" in summary_text
-
-    def test_double_carry_shares_the_budget(self, tmp_db, mock_openai_client):
-        """Spill + hint on ONE compaction — the end-of-turn shape — must fit
-        the window together.  At the shipped window defaults each carry gets
-        spare // 2, so two oversize carries land truncated to the shared
-        budget instead of stacking two solo quarter-window allowances on top
-        of the half-window summary reserve."""
-        s = make_session(client=mock_openai_client, tool_timeout=10)
-        per_carry = s._carry_budget_chars(2)
-        ask = "ASK-HEAD " + "a" * (per_carry * 2) + " ASK-TAIL"
-        spill = "PLAN-HEAD " + "b" * (per_carry * 2) + " PLAN-TAIL"
-        s.messages = turns_from_dicts(
-            [
-                {"role": "user", "content": ask},
-                {"role": "assistant", "content": spill},
-            ]
-        )
-        s._msg_tokens = [1, 1]
-        with patch.object(s, "_utility_completion", return_value=_stub_summary()):
-            assert s._compact_messages(auto=True, carry_spill=True) is True
-
-        text = s.messages[1].text or ""
-        assert "## Wind-down (verbatim)" in text and "## Continue" in text
-        for sentinel in ("ASK-HEAD", "ASK-TAIL", "PLAN-HEAD", "PLAN-TAIL"):
-            assert sentinel in text
-        assert text.count("…[truncated —") == 2  # both carries hit the shared cap
-        framing = 700  # headings, hint wording, stub summary, recall pointer
-        assert len(text) <= 2 * per_carry + framing
-
-    def test_do_auto_compact_forwards_carry_spill(self, session):
-        """The end-of-turn site passes carry_spill=stopped_to_compact through
-        _do_auto_compact — pin the forwarding."""
-        with patch.object(session, "_compact_messages", return_value=True) as cm:
-            session._do_auto_compact(my_generation=3, carry_spill=True)
-        assert cm.call_args.kwargs["carry_spill"] is True
-        assert cm.call_args.kwargs["my_generation"] == 3

tests/test_console.py

@@ -4,7 +4,7 @@ import asyncio
 import json
 import queue
 from typing import Any
-from unittest.mock import ANY, MagicMock
+from unittest.mock import MagicMock
 
 import pytest
 
@@ -531,58 +531,6 @@ class TestCollectorDelta:
         assert event["type"] == "ws_closed"
         assert "ws1" not in c._nodes["node-a"].workstreams
 
-    def test_reconcile_additions_event_carries_tenancy_fields(self):
-        """The poll-diff ws_created must carry user_id + project_id — the
-        console's per-connection tenancy filter gates on them, and a
-        missing field fails open (private leak) or over-hides (creator
-        shortcut can't fire)."""
-        c = _make_collector()
-        node = NodeSnapshot(node_id="node-a", server_url="http://a:8080")
-        c._nodes["node-a"] = node
-
-        pending = c._reconcile_node(
-            "node-a",
-            node,
-            [
-                {
-                    "id": "ws1",
-                    "name": "n",
-                    "state": "idle",
-                    "kind": "interactive",
-                    "user_id": "alice",
-                    "project_id": "p1",
-                }
-            ],
-        )
-
-        created = [e for e in pending if e["type"] == "ws_created"]
-        assert len(created) == 1
-        assert created[0]["user_id"] == "alice"
-        assert created[0]["project_id"] == "p1"
-
-    def test_emit_console_ws_created_carries_project(self):
-        """Console pseudo-node coordinator rows + their ws_created must
-        carry project_id or private-project coordinators leak on the
-        SSE surface (the REST lane filters via _coordinator_rows)."""
-        c = _make_collector()
-        q: queue.Queue[dict] = queue.Queue()
-        c.register_listener(q)
-
-        c.emit_console_ws_created(
-            "cws1",
-            name="C",
-            user_id="alice",
-            kind="coordinator",
-            project_id="p1",
-        )
-
-        event = q.get_nowait()
-        assert event["type"] == "ws_created"
-        assert event["user_id"] == "alice"
-        assert event["project_id"] == "p1"
-        row = c._nodes[c.CONSOLE_PSEUDO_NODE_ID].workstreams["cws1"]
-        assert row["project_id"] == "p1"
-
     def test_apply_delta_ws_rename(self):
         c = _make_collector()
         c._nodes["node-a"] = NodeSnapshot(
@@ -1098,8 +1046,6 @@ class TestConsoleHTTPEndpoints:
             page=1,
             per_page=25,
             extra_rows=[],
-            # Per-request private-project tenancy closure — identity varies.
-            row_filter=ANY,
         )
 
     def test_get_workstreams_per_page_capped(self, client, mock_collector):

tests/test_console_available_models.py

@@ -336,88 +336,10 @@ def test_channel_default_alias_blanked_when_disabled(
 
 
 def test_models_payload_strips_secret_fields(storage: SQLiteBackend) -> None:
-    """Regression guard: only alias/model/provider (+ the derived
-    effort_ladder) land in the response, never api_key / base_url /
-    context_window / raw capabilities."""
+    """Regression guard: only alias/model/provider land in the response,
+    never api_key / base_url / context_window / capabilities."""
     _seed_model(storage, definition_id="m1", alias="primary")
     body = _get_models(_make_client(storage))
-    assert len(body["models"]) == 1
-    entry = body["models"][0]
-    assert set(entry) == {"alias", "model", "provider", "effort_ladder"}
-    assert entry["alias"] == "primary"
-    assert entry["model"] == "model-x"
-    assert entry["provider"] == "openai-compatible"
-
-
-def test_effort_ladder_parses_string_capabilities(storage: SQLiteBackend) -> None:
-    """The capabilities column is a JSON STRING — the ladder must survive
-    the parse (regression: .items() on the raw string threw and the
-    guard silently dropped the field from every row)."""
-    storage.create_model_definition(
-        definition_id="m1",
-        alias="qwen",
-        model="qwen3.6-27b",
-        provider="anthropic-compatible",
-        base_url="http://localhost:8000",
-        api_key="dummy",
-        context_window=262144,
-        capabilities='{"thinking_mode": "manual", "thinking_param": "enable_thinking"}',
-        enabled=True,
-        created_by="admin",
-    )
-    body = _get_models(_make_client(storage))
-    ladder = {r["value"]: r["effective"] for r in body["models"][0]["effort_ladder"]}
-    assert ladder["none"] == "off"
-    assert ladder["medium"] == "on+medium"
-    assert ladder["max"] == "on+max"
-
-
-def test_effort_ladder_key_survives_malformed_capabilities(
-    storage: SQLiteBackend,
-) -> None:
-    """A capabilities column that fails to parse must not drop the key —
-    every row carries ``effort_ladder`` (empty on failure) so clients can
-    index it unconditionally instead of null-checking per row."""
-    storage.create_model_definition(
-        definition_id="m1",
-        alias="broken",
-        model="model-x",
-        provider="openai-compatible",
-        base_url="http://localhost:8000/v1",
-        api_key="dummy",
-        context_window=131072,
-        capabilities="{not valid json",
-        enabled=True,
-        created_by="admin",
-    )
-    body = _get_models(_make_client(storage))
-    entry = body["models"][0]
-    assert set(entry) == {"alias", "model", "provider", "effort_ladder"}
-    assert entry["effort_ladder"] == []
-
-
-def test_effort_ladder_honors_responses_api_surface(storage: SQLiteBackend) -> None:
-    """server_compat.api_surface (namespaced inside the capabilities JSON)
-    switches the projection to the flat-param path — no template toggle."""
-    caps = (
-        '{"thinking_mode": "manual", "thinking_param": "enable_thinking",'
-        ' "reasoning_effort_values": ["low", "medium", "high"],'
-        ' "server_compat": {"api_surface": "responses"}}'
-    )
-    storage.create_model_definition(
-        definition_id="m1",
-        alias="mistral",
-        model="mistral-medium",
-        provider="openai-compatible",
-        base_url="http://localhost:8000/v1",
-        api_key="dummy",
-        context_window=131072,
-        capabilities=caps,
-        enabled=True,
-        created_by="admin",
-    )
-    body = _get_models(_make_client(storage))
-    ladder = {r["value"]: r["effective"] for r in body["models"][0]["effort_ladder"]}
-    # Responses surface: flat param only — no "on+"/"off" toggle tokens.
-    assert ladder["medium"] == "medium"
-    assert ladder["none"] == "default"
+    assert body["models"] == [
+        {"alias": "primary", "model": "model-x", "provider": "openai-compatible"}
+    ]

tests/test_console_effort_ladder.py

@@ -1,106 +0,0 @@
-"""``POST /v1/api/admin/models/effort-ladder`` — live modal projection.
-
-Pure computation over (provider, model, unsaved capability overrides,
-api_surface); every malformed input must land as a 400, never a 500 —
-the body is operator-typed form state.
-"""
-
-from __future__ import annotations
-
-from typing import Any
-
-from starlette.applications import Starlette
-from starlette.middleware import Middleware
-from starlette.routing import Route
-from starlette.testclient import TestClient
-
-from tests._coord_test_helpers import _AuthMiddleware
-from turnstone.console.server import admin_effort_ladder
-
-
-def _make_client() -> TestClient:
-    app = Starlette(
-        routes=[Route("/v1/api/admin/models/effort-ladder", admin_effort_ladder, methods=["POST"])],
-        middleware=[Middleware(_AuthMiddleware)],
-    )
-    client = TestClient(app)
-    client.headers.update({"X-Test-User": "admin", "X-Test-Perms": "admin.models"})
-    return client
-
-
-def _post(client: TestClient, body: Any) -> Any:
-    return client.post("/v1/api/admin/models/effort-ladder", json=body)
-
-
-def test_valid_request_returns_ladder() -> None:
-    resp = _post(
-        _make_client(),
-        {
-            "provider": "anthropic-compatible",
-            "model": "qwen3.6-27b",
-            "capabilities": {"thinking_mode": "manual", "thinking_param": "enable_thinking"},
-        },
-    )
-    assert resp.status_code == 200, resp.text
-    ladder = {r["value"]: r["effective"] for r in resp.json()["ladder"]}
-    assert ladder["none"] == "off"
-    assert ladder["high"] == "on+high"
-
-
-def test_api_surface_switches_projection() -> None:
-    body = {
-        "provider": "openai-compatible",
-        "model": "m",
-        "capabilities": {
-            "thinking_mode": "manual",
-            "reasoning_effort_values": ["low", "medium", "high"],
-        },
-    }
-    client = _make_client()
-    chat = {r["value"]: r["effective"] for r in _post(client, body).json()["ladder"]}
-    body["api_surface"] = "responses"
-    responses = {r["value"]: r["effective"] for r in _post(client, body).json()["ladder"]}
-    assert chat["medium"] == "on+medium"  # toggle + flat on the chat surface
-    assert responses["medium"] == "medium"  # flat only on the responses surface
-
-
-def test_non_dict_json_body_is_400_not_500() -> None:
-    client = _make_client()
-    for body in (None, [], "x", 7):
-        resp = _post(client, body)
-        assert resp.status_code == 400, (body, resp.status_code, resp.text)
-
-
-def test_unknown_provider_is_400() -> None:
-    resp = _post(_make_client(), {"provider": "nope", "model": "m"})
-    assert resp.status_code == 400
-
-
-def test_missing_model_is_400() -> None:
-    resp = _post(_make_client(), {"provider": "openai", "model": ""})
-    assert resp.status_code == 400
-
-
-def test_non_dict_capabilities_is_400() -> None:
-    resp = _post(_make_client(), {"provider": "openai", "model": "m", "capabilities": [1]})
-    assert resp.status_code == 400
-
-
-def test_garbage_capability_value_types_are_400() -> None:
-    """Wrong-typed override values raise inside the resolver → clean 400."""
-    resp = _post(
-        _make_client(),
-        {
-            "provider": "anthropic",
-            "model": "claude-fable-5",
-            "capabilities": {"supports_effort": True, "effort_levels": 5},
-        },
-    )
-    assert resp.status_code == 400
-
-
-def test_requires_admin_models_permission() -> None:
-    client = _make_client()
-    client.headers.update({"X-Test-Perms": "read"})
-    resp = _post(client, {"provider": "openai", "model": "m"})
-    assert resp.status_code in (401, 403)

tests/test_console_routing_proxy.py

@@ -347,38 +347,6 @@ class TestClusterCreate:
         assert payload[0] == "a.txt" and payload[1] == b"hello world"
         client.close()
 
-    def test_cluster_create_forwards_project_id(self) -> None:
-        # Phase 6: the launcher's project picker sends project_id; the proxy
-        # selectively REBUILDS the forwarded body (it doesn't pass it through),
-        # so project_id must be explicitly carried or the node never scopes the
-        # session to its project.
-        mock_post = _make_proxy_post(json_data={"ws_id": "p1ws"})
-        client = TestClient(self._app_with_node(mock_post), raise_server_exceptions=False)
-        resp = client.post(
-            "/v1/api/cluster/workstreams/new",
-            json={"node_id": "node-a", "name": "j", "project_id": "proj-42"},
-            headers=_TEST_AUTH_HEADERS,
-        )
-        assert resp.status_code == 200
-        assert mock_post.call_args.kwargs["json"]["project_id"] == "proj-42"
-        client.close()
-
-    def test_cluster_create_forwards_persona(self) -> None:
-        # The launcher's persona picker sends persona; the proxy selectively
-        # REBUILDS the forwarded body (it doesn't pass it through), so persona
-        # must be explicitly carried or the receiving node stamps its kind
-        # default instead of the operator's choice.
-        mock_post = _make_proxy_post(json_data={"ws_id": "p1ws"})
-        client = TestClient(self._app_with_node(mock_post), raise_server_exceptions=False)
-        resp = client.post(
-            "/v1/api/cluster/workstreams/new",
-            json={"node_id": "node-a", "name": "j", "persona": "scribe"},
-            headers=_TEST_AUTH_HEADERS,
-        )
-        assert resp.status_code == 200
-        assert mock_post.call_args.kwargs["json"]["persona"] == "scribe"
-        client.close()
-
 
 # ---------------------------------------------------------------------------
 # Tests — route_proxy

tests/test_conversation_js.py

@@ -150,21 +150,3 @@ def test_warning_and_verdict_normalize_risk() -> None:
     assert "normalizeRiskLevel(a.risk_level)" in body, "warning must normalize"
     assert '"conv-warning conv-warning--" + risk' in body
     assert 'badge.classList.add("conv-verdict--" + risk)' in body
-
-
-def test_unbounded_render_inputs_are_capped() -> None:
-    """Perf-audit P0: the two builders that used to render unbounded input.
-    The diff preview caps rendered lines and appends incrementally — the old
-    single ``diff.append(...nodes)`` spread threw RangeError past engine
-    spread-arity limits, killing the tool card (and the approval gate) for
-    the batch.  The raw result body clamps at RAW_CAP so one multi-MB tool
-    output can't become a multi-MB pre-wrap text node rebuilt on every
-    re-render."""
-    body = _body()
-    assert "MAX_PREVIEW_LINES" in body
-    assert "diff.append(...nodes)" not in body, (
-        "preview nodes must append incrementally, not via one spread call"
-    )
-    assert "more preview lines not shown" in body
-    assert "RAW_CAP" in body
-    assert "truncated for display" in body

tests/test_coord_ui_approve_tools.py

@@ -16,10 +16,10 @@ to ``SessionUIBase`` automatically enables:
 
 from __future__ import annotations
 
+import threading
 from typing import Any
 from unittest.mock import MagicMock, patch
 
-from tests.conftest import resolve_when_pending
 from turnstone.console.coordinator_ui import ConsoleCoordinatorUI
 
 
@@ -153,7 +153,7 @@ def test_coord_heuristic_verdict_persists_to_storage() -> None:
     items[0]["_heuristic_verdict"] = hv
 
     storage = MagicMock()
-    timer = resolve_when_pending(ui, False)
+    timer = threading.Timer(0.05, lambda: ui.resolve_approval(False))
     timer.start()
     try:
         with _patch_storage(storage):
@@ -246,8 +246,9 @@ def test_coord_pending_approval_sets_activity_tag() -> None:
     def _capture_activity() -> None:
         captured["activity"] = ui._ws_current_activity
         captured["state"] = ui._ws_activity_state
+        ui.resolve_approval(False)
 
-    timer = resolve_when_pending(ui, False, before=_capture_activity)
+    timer = threading.Timer(0.05, _capture_activity)
     timer.start()
     try:
         with _patch_storage(MagicMock()):
@@ -291,7 +292,7 @@ def test_coord_judge_pending_flag_dynamic_when_heuristic_present() -> None:
     captured_events: list[dict[str, Any]] = []
     ui._enqueue = captured_events.append  # type: ignore[method-assign]
 
-    timer = resolve_when_pending(ui, False)
+    timer = threading.Timer(0.05, lambda: ui.resolve_approval(False))
     timer.start()
     try:
         with _patch_storage(MagicMock()):
@@ -337,7 +338,7 @@ def test_coord_judge_pending_false_when_no_heuristic_verdict() -> None:
     captured_events: list[dict[str, Any]] = []
     ui._enqueue = captured_events.append  # type: ignore[method-assign]
 
-    timer = resolve_when_pending(ui, False)
+    timer = threading.Timer(0.05, lambda: ui.resolve_approval(False))
     timer.start()
     try:
         with _patch_storage(MagicMock()):
@@ -409,7 +410,7 @@ def test_coord_budget_override_prompts_even_under_blanket_auto_approve() -> None
     captured_events: list[dict[str, Any]] = []
     ui._enqueue = captured_events.append  # type: ignore[method-assign]
 
-    timer = resolve_when_pending(ui, True)
+    timer = threading.Timer(0.05, lambda: ui.resolve_approval(True))
     timer.start()
     try:
         with _patch_storage(MagicMock()):
@@ -452,7 +453,7 @@ def test_coord_budget_override_survives_wildcard_allow_policy() -> None:
     captured_events: list[dict[str, Any]] = []
     ui._enqueue = captured_events.append  # type: ignore[method-assign]
 
-    timer = resolve_when_pending(ui, True)
+    timer = threading.Timer(0.05, lambda: ui.resolve_approval(True))
     timer.start()
     try:
         with _patch_storage(MagicMock()), _patch_policies({"__budget_override__": "allow"}):
@@ -525,16 +526,12 @@ class TestBroadcastApprovalResolved:
         collector = MagicMock()
         ConsoleCoordinatorUI._collector = collector
         try:
-            ui._broadcast_approval_resolved(
-                True, "lgtm", always=True, cycle_id="cyc-1", call_ids=("c-1", "c-2")
-            )
+            ui._broadcast_approval_resolved(True, "lgtm", always=True)
             collector.emit_console_ws_approval_resolved.assert_called_once_with(
                 "coord-a",
                 approved=True,
                 feedback="lgtm",
                 always=True,
-                cycle_id="cyc-1",
-                call_ids=["c-1", "c-2"],
             )
         finally:
             ConsoleCoordinatorUI._collector = None
@@ -550,8 +547,6 @@ class TestBroadcastApprovalResolved:
                 approved=False,
                 feedback="",
                 always=False,
-                cycle_id="",
-                call_ids=[],
             )
         finally:
             ConsoleCoordinatorUI._collector = None

tests/test_coordinator_adapter.py

@@ -73,7 +73,7 @@ def _make_ws(**overrides: Any) -> Workstream:
 
 def test_emit_created_calls_collector_with_coord_fields() -> None:
     adapter, collector = _make_adapter()
-    ws = _make_ws(project_id="p1", persona="executive")
+    ws = _make_ws()
     adapter.emit_created(ws)
     collector.emit_console_ws_created.assert_called_once_with(
         "coord-1",
@@ -82,10 +82,6 @@ def test_emit_created_calls_collector_with_coord_fields() -> None:
         kind=WorkstreamKind.COORDINATOR.value,
         state=WorkstreamState.IDLE.value,
         parent_ws_id=None,
-        # Tenancy-load-bearing: the console SSE filter gates on this.
-        project_id="p1",
-        # Display carrier: the pseudo-node row + ws_created event wear it.
-        persona="executive",
     )
 
 
@@ -195,21 +191,6 @@ def test_emit_tolerates_collector_exception() -> None:
 # ---------------------------------------------------------------------------
 
 
-def test_cleanup_ui_sweeps_all_approval_cycles_on_registry_uis() -> None:
-    """The real ConsoleCoordinatorUI carries the approval-cycle
-    registry: cleanup denies + wakes EVERY parked gate via
-    ``resolve_all_approvals`` (parallel task agents can hold several),
-    not the pre-cycle single-slot kick."""
-    adapter, _ = _make_adapter()
-    ws = _make_ws()
-    ws.ui.resolve_all_approvals = MagicMock(return_value=2)  # type: ignore[attr-defined]
-    adapter.cleanup_ui(ws)
-    ws.ui.resolve_all_approvals.assert_called_once_with(  # type: ignore[attr-defined]
-        False, "Workstream closed"
-    )
-    assert ws.ui._fg_event.is_set()  # type: ignore[attr-defined]
-
-
 def test_cleanup_ui_unblocks_events_and_broadcasts_to_listeners() -> None:
     adapter, _ = _make_adapter()
     ws = _make_ws()
@@ -306,7 +287,6 @@ class _SendSession:
     ) -> None:
         self.send_calls: list[str] = []
         self.queue_calls: list[str] = []
-        self.interjector_ids: list[str] = []
         self._queue_full = queue_full
         # When set, ``send`` blocks on this event — lets the test pin a
         # worker inside session.send while a second thread races through
@@ -333,11 +313,9 @@ class _SendSession:
         message: str,
         attachment_ids: Any = None,
         queue_msg_id: str | None = None,
-        interjector_user_id: str = "",
     ) -> None:
         if self._queue_full:
             raise queue.Full
-        self.interjector_ids.append(interjector_user_id)
         self.queue_calls.append(message)
 
     def cancel(self) -> None:

tests/test_coordinator_close_all_children.py

@@ -1,7 +1,8 @@
 """Tests for the coordinator ``close_all_children`` endpoint.
 
-Keeps the close-cascade surface in its own file so the review surface
-stays tight.
+Near-twin of the ``stop_cascade`` tests in
+``test_coordinator_governance.py``.  Keeps the close-cascade surface in
+its own file so PR A's review surface stays tight.
 """
 
 from __future__ import annotations
@@ -218,7 +219,7 @@ def test_close_all_children_404_when_session_not_loaded(storage):
 def test_close_all_children_service_token_cannot_bypass_admin_coordinator(storage):
     """Destructive endpoint — a service token matching the coord owner
     still needs the explicit ``admin.coordinator`` grant.  Mirrors the
-    ``restrict`` treatment."""
+    stop_cascade treatment."""
     mgr = _build_mgr(storage)
     coord = mgr.create(user_id="user-1", name="coord-a")
     coord.session = MagicMock()