openclaw/docs/tools/skill-workshop.md

summary, read_when, title, sidebarTitle

summary	read_when	title	sidebarTitle
Create and update workspace skills through Skill Workshop review	You want the agent to create or update a skill from chat You need to review, apply, reject, or quarantine a generated skill draft You are configuring Skill Workshop approval, autonomy, storage, or limits You want to understand where self-learning proposals are reviewed	Skill Workshop	Skill Workshop

Skill Workshop is OpenClaw's governed path for creating and updating workspace skills. Through this path, agents and operators create a proposal (pending draft with content, target binding, scanner state, hashes, and rollback metadata) that becomes a live skill only when applied.

Skill Workshop writes workspace skills only. It never touches bundled, plugin, ClawHub, extra-root, managed, personal-agent, or system skills.

How it works

• Proposal first: generated content is stored as PROPOSAL.md, not SKILL.md.
• Apply is the only live write: create, update, and revise never change active skills.
• Workspace scoped: creates target the workspace skills/ root; updates are allowed only for writable workspace skills.
• No clobber: create fails if the target skill already exists.
• Hash bound: update proposals bind to the current target hash and go stale if the live skill changes before apply.
• Scanner gated: apply reruns the security scanner before writing. Only critical findings block apply; warn-level findings remain visible but do not block it.
• Recoverable: apply writes rollback metadata before touching live files.
• Consistent surfaces: chat, CLI, and Gateway all call the same service.

Lifecycle

create/update -> pending
revise        -> pending
evaluate      -> pending
apply         -> applied
reject        -> rejected
quarantine    -> quarantined
target change -> stale

Only a pending proposal can be revised, applied, rejected, or quarantined.

Lifecycle curation

The Gateway tracks aggregate skill usage in the shared state database. Once a day, it reviews applied skills created through agent autocapture. Skills unused for more than 30 days become stale; after 90 days they become archived and are left out of new agent skill snapshots. Archived skill files remain unchanged on disk. Operator-created skills, including proposals created through the CLI or Gateway/Control UI, are treated as manual and never curated.

Pinned skills bypass lifecycle transitions. A stale skill returns to active after it is used and the next sweep runs. Archived skills return only through an explicit restore:

Lifecycle transitions and restores apply to new sessions; running sessions keep their current skill snapshot.

openclaw skills curator status
openclaw skills curator pin <skill>
openclaw skills curator unpin <skill>
openclaw skills curator restore <skill>

All curator commands accept --json. Status also reports deterministic overlap candidates as suggestions only; it never merges skills or calls a model.

Chat

Ask the agent for the skill you want; it calls skill_workshop and returns a proposal id.

Learn from recent work

Use /learn to turn the current conversation or named sources into one standards-guided skill proposal:

/learn
/learn docs/runbook.md and https://example.com/guide; focus on recovery

With no request, /learn asks the agent to distill the reusable workflow from the current conversation. With a request, the agent treats paths, URLs, pasted notes, and conversation references as sources while honoring focus, scope, and naming requirements. It gathers the sources with its existing tools, then calls skill_workshop with action: "create".

The resulting proposal stays pending; /learn never applies it. Review and apply it through the normal approval flow or with openclaw skills workshop.

Create:

Make a skill called morning-catchup that runs my Monday inbox routine.

Update an existing workspace skill:

Update trip-planning to also check seat maps before booking.

If a skill used in the current turn proves wrong or incomplete, the agent reads the live skill and creates a targeted patch proposal. A runtime receipt limits this flow to skills used in that run. Autonomous mode off disables repair, propose leaves the patch pending until explicitly applied, and auto scans and applies it immediately. The repaired skill is loaded by new sessions; the running session keeps its original skill snapshot.

Iterate on a pending proposal:

Show me the morning-catchup proposal.
Revise it to also flag anything marked urgent.
Apply the morning-catchup proposal.

Agent-initiated apply, reject, and quarantine run without an additional approval prompt by default. Set skills.workshop.approvalPolicy to "pending" to require operator approval before those actions.

When approval is required, the prompt identifies the proposal id and target skill, and shows the proposal description, support-file count, and body size. Approval requests are bounded to finish before the agent tool watchdog. If no decision arrives before the prompt expires, the lifecycle action does not run: the proposal stays pending and unchanged. Decide later in the Skill Workshop UI or run openclaw skills workshop apply|reject|quarantine <proposal-id>. Agents should not retry an expired lifecycle action in a loop.

CLI

# Create
openclaw skills workshop propose-create \
  --name morning-catchup \
  --description "Daily inbox catch-up: triage, archive, surface, draft, plan" \
  --proposal ./PROPOSAL.md

# Update an existing workspace skill
openclaw skills workshop propose-update trip-planning --proposal ./PROPOSAL.md

# List and inspect
openclaw skills workshop list
openclaw skills workshop inspect <proposal-id>

# Revise before approval
openclaw skills workshop revise <proposal-id> --proposal ./PROPOSAL.md

# Run installed plugin evaluators against the exact current draft
openclaw skills workshop evaluate <proposal-id>

# Close out
openclaw skills workshop apply <proposal-id>
openclaw skills workshop reject <proposal-id> --reason "Duplicate"
openclaw skills workshop quarantine <proposal-id> --reason "Needs security review"

Every subcommand takes --agent <id> (target workspace; defaults to cwd-inferred, then the default agent) and --json (structured output). propose-create, propose-update, and revise also take --goal <text> and --evidence <text> to record proposal context alongside --proposal. evaluate runs through the live Gateway plugin registry, snapshots the current proposal revision before dispatch, and accepts --correlation-id <id> for external orchestration.

Plugin evaluation and lifecycle hooks

Gateway plugins can extend Skill Workshop without owning proposal storage or live skill writes:

• skill_proposal_evaluate receives an exact candidate bundle and, for update proposals, the complete baseline skill. It returns attributed findings, metrics, and an optional pass, revise, or block decision.
• skill_proposal_changed observes durable created, revised, evaluation_completed, applied, rejected, quarantined, and stale events.
• skill_changed observes committed live skill created, updated, and removed events from Workshop and supported install/uninstall paths.

Evaluations are explicit from the CLI, Control UI, Gateway skills.proposals.evaluate method, or agent skill_workshop action. Results are stored on the exact proposal revision and in the append-only proposal event ledger. Evaluator failures remain attributed results; only a completed decision: "block" prevents apply. Apply also revalidates the evaluated target tree, so any live skill asset drift requires a fresh evaluation.

The lifecycle supports external optimization loops without embedding one. Controllers can consume skills.proposals.events.list, evaluate an exact revisionHash, revise with expectedRevisionHash and correlationId, then continue from the returned event sequence. OpenClaw does not schedule, auto-revise, or decide when such a loop should stop.

Proposal content

While pending, the proposal is stored as PROPOSAL.md with proposal-only frontmatter:

---
name: "morning-catchup"
description: "Daily inbox catch-up: triage, archive, surface, draft, plan"
status: proposal
version: "v1"
date: "2026-05-30T00:00:00.000Z"
---

On apply, Skill Workshop writes the active SKILL.md and removes the proposal-only fields: status, proposal version, and proposal date.

Support files

Use --proposal-dir when the proposed skill needs files beside PROPOSAL.md:

openclaw skills workshop propose-create \
  --name weekly-update \
  --description "Friday wrap-up: stats, highlights, next week's top three" \
  --proposal-dir ./weekly-update-proposal

The directory must contain PROPOSAL.md. Support files must live under assets/, examples/, references/, scripts/, or templates/. Skill Workshop scans, hashes, and stores them with the proposal, then writes them beside the live SKILL.md only on apply.

Rejected support-file paths: absolute paths, hidden path segments, path traversal, overlapping paths, executable files, non-UTF-8 text, null bytes, and paths outside the standard support folders.

Agent tool

The model uses skill_workshop with one required action: create | read | patch | update | revise | list | inspect | evaluate | apply | reject | quarantine. Other parameters apply depending on the action:

Parameter	Used by	Notes
name	create, inspect, revise	Required for create; resolves a pending proposal by name otherwise
description	create, update, revise	Max 160 bytes
skill_name	read, patch, update	Existing skill name or key
old_string, new_string	patch	Exact current text and its replacement; read the skill first
proposal_content	create, update, revise	Required for create/update; omit on revise to preserve the body
support_files	create, update, revise	Array of { path, content }
goal, evidence	create, update, revise	Free-text context
proposal_id	inspect, revise, evaluate, apply, reject, quarantine	Target proposal
expected_revision_hash	evaluate, apply, reject, quarantine	Rejects a stale orchestration step
correlation_id	evaluate, revise, apply, reject, quarantine	External run or experiment correlation
reason	apply, reject, quarantine	Optional
query, status, limit	list	Filter/paginate; limit max 50, default 20

Agents must use skill_workshop for generated skill work and must not create or change skill or proposal files directly. This rule is advisory and prompt-enforced. A hard guard is not currently possible at the tool-policy seam.

`skill_workshop` is a built-in agent tool and is included in `tools.profile: "coding"`. If a stricter policy hides it, add `skill_workshop` to the active `tools.allow` list, or use `tools.alsoAllow: ["skill_workshop"]` when the scope uses a profile without an explicit `tools.allow`. Sandboxed runs do not construct the host-side Skill Workshop tool, so run proposal review actions from a normal host-side agent session or the CLI.

Self-learning

After substantial work, an isolated background review can turn corrections and successful procedures into Workshop proposals; see Self-learning. Set skills.workshop.autonomous.mode to propose to create pending proposals, or to auto to apply scanner-approved captures through the normal Workshop service. The Control UI Workshop tab shows whether self-learning is on; use the config setting to choose all three modes.

Scan past sessions

The Control UI can review older work without enabling autonomous self-learning. Open Plugins → Workshop and select Find skill ideas. The scan starts with the newest eligible sessions and reviews a bounded window of substantial work. It skips cron, heartbeat, hook, subagent, ACP, plugin-owned, and internal review sessions, plus conversations with fewer than six model turns.

The reviewer uses the selected agent's configured model and receives a secret-redacted, size-bounded transcript bundle. It applies the same conservative bar as experience review: a concrete recovery pattern or a stable procedure that would remove at least two future model or tool calls. Routine work and one-off facts should produce no proposal.

One scan can create or revise at most three pending proposals. It cannot apply, reject, quarantine, or edit a live skill. The Workshop shows cumulative coverage, for example 20 sessions reviewed · Jun 18–today · 2 ideas found. Select Scan earlier work to continue from the persisted oldest-session cursor. After the available history is exhausted, the action becomes Scan new work.

Historical review is manual even when skills.workshop.autonomous.mode is off. Each click starts a model run, so provider pricing and data-handling terms apply. The cursor and coverage counts are stored in the shared OpenClaw state database; transcript content is not copied into scan state.

In propose and auto modes, OpenClaw can also perform a conservative review after successful, substantial work and after the whole agent system becomes idle. The review receives an authoritative receipt of skills the foreground run actually used. It can draft at most one pending proposal: a new skill, a patch or full-body rewrite of an existing workspace skill, or a revision of a pending proposal. Existing skills must be read before either update form, and the proposal is bound to that exact content hash. The reviewer never writes a live skill directly and cannot apply, reject, or quarantine a proposal. In auto mode, the orchestrating pipeline applies every autonomous result afterward through the normal scanner-gated service, without operator review.

See Self-learning for enablement, eligibility, privacy and cost details, the proposal threshold, and troubleshooting.

Approval and autonomy

{
  skills: {
    workshop: {
      autonomous: {
        mode: "auto",
      },
      allowSymlinkTargetWrites: false,
      approvalPolicy: "auto",
      maxPending: 50,
      maxSkillBytes: 40000,
    },
  },
}

Setting	Default	Effect
autonomous.mode	"auto"	"off" disables autonomous capture, "propose" creates pending captures, and "auto" applies captures through the normal Workshop scanner and apply path.
allowSymlinkTargetWrites	false	Lets apply write through workspace skill symlinks whose real target is listed in skills.load.allowSymlinkTargets.
approvalPolicy	"auto"	"auto" skips an additional prompt for agent-initiated apply, reject, or quarantine (the agent still has to call the action). "pending" requires approval.
maxPending	50	Caps pending and quarantined proposals per workspace (1-200).
maxSkillBytes	40000	Caps proposal body size in bytes (1024-200000).

In propose and auto modes, an isolated run of the selected model decides whether the completed trajectory clears the evidence-gated proposal bar. The foreground model is not prompted to learn before it replies. The background reviewer preserves the foreground run as proposal provenance, cannot access general agent tools, and cannot make lifecycle decisions. In auto mode, the capture pipeline applies every autonomous proposal only after the isolated run completes. Existing-skill changes require a complete read receipt and content-hash binding before they are eligible for that apply step. The review starts only when the foreground runtime reports its resolved model and that skill_workshop was actually available. Restrictive or unknown tool policy therefore fails closed and creates no proposal.

See Self-learning for the complete autonomous review behavior and safety model.

Proposal descriptions are always capped at 160 bytes, independent of maxSkillBytes.

Gateway methods

Method	Scope
skills.proposals.list	operator.read
skills.proposals.inspect	operator.read
skills.proposals.historyStatus	operator.read
skills.proposals.historyScan	operator.admin
skills.proposals.create	operator.admin
skills.proposals.update	operator.admin
skills.proposals.revise	operator.admin
skills.proposals.requestRevision	operator.admin
skills.proposals.apply	operator.admin
skills.proposals.reject	operator.admin
skills.proposals.quarantine	operator.admin
skills.curator.status	operator.read
skills.curator.pin	operator.admin
skills.curator.unpin	operator.admin
skills.curator.restore	operator.admin

requestRevision is Gateway-only (no CLI or agent-tool equivalent): it forwards free-text revision instructions to the owning agent's chat session instead of replacing PROPOSAL.md directly, for UIs that ask the agent to revise rather than submit literal new content.

historyStatus and historyScan are Control UI support methods. historyScan accepts direction: "older" | "newer"; it always leaves results as pending proposals.

Storage

<OPENCLAW_STATE_DIR>/
  state/openclaw.sqlite
  skill-workshop/proposals/<proposal-id>/
    PROPOSAL.md
    assets/
    examples/
    references/
    scripts/
    templates/

Default state directory: ~/.openclaw.

• state/openclaw.sqlite: canonical proposal records, lifecycle status, origin attribution, and apply rollback metadata.
• PROPOSAL.md: pending skill proposal.
• Support files remain beside PROPOSAL.md so operators can review the proposed skill as a normal directory.

openclaw doctor --fix imports the previous proposals.json, proposal.json, and rollback.json metadata into SQLite after verifying each proposal, then removes the migrated JSON files. If an agent's configured workspace changes, its earlier proposals remain listed with a previous-workspace marker instead of disappearing.

Limits

Limit	Value
Description	160 bytes
Proposal body	skills.workshop.maxSkillBytes (default 40,000; hard ceiling 200,000 bytes)
Support files	64 per proposal
Support file size	256 KiB each, 2 MiB total
Pending + quarantined proposals	skills.workshop.maxPending per workspace (default 50)

Troubleshooting

Problem	Resolution
Skill proposal description is too large	Shorten description to 160 bytes or less.
Skill proposal content is too large	Shorten the proposal body or raise skills.workshop.maxSkillBytes.
Target skill changed after proposal creation	Revise the proposal against the current target, or create a new proposal.
Proposal scan failed	Inspect scanner findings, then revise or quarantine the proposal.
untrusted symlink target	Configure skills.load.allowSymlinkTargets and enable skills.workshop.allowSymlinkTargetWrites only for intentional shared skill roots.
Support file paths must be under one of...	Move support files under assets/, examples/, references/, scripts/, or templates/.
Proposal does not show in list	Check the selected --agent workspace and OPENCLAW_STATE_DIR.
Agent cannot call skill_workshop	Check the active tool policy and run mode. coding includes the tool; restrictive tools.allow policies must list it explicitly, and sandboxed runs must use a normal host-side agent session or the CLI.

Tool-policy diagnostic

In propose and auto modes, openclaw doctor runs the core/doctor/skill-workshop-tool-policy check for the default agent. If policy hides skill_workshop, the warning names the first excluding config layer and the exact allow or alsoAllow change to make. Older runbooks may still use openclaw plugins inspect skill-workshop; that command now explains that Skill Workshop is built in and prints the same policy hint when applicable.

Related

• Skills for load order, precedence, and visibility
• Self-learning for conservative post-run skill proposals
• Creating skills for hand-written SKILL.md basics
• Skills config for the full skills.workshop schema
• Skills CLI for openclaw skills commands