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.

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

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`	`update`	Existing skill name or key
`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.

Suggested skills

OpenClaw detects durable instructions such as “next time,” “remember to,” and reactive corrections when an interactive turn ends, including failed turns. On the next turn, the agent offers to save the most recent detected workflow through skill_workshop; the user decides whether to create a proposal. This built-in suggestion does not create or change a skill by itself. Set skills.workshop.autonomous.mode to propose to create pending proposals directly, 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. That isolated review can create or revise at most one pending proposal. It cannot update a live skill or apply, reject, or quarantine a proposal. In auto mode, the orchestrating capture pipeline applies the result afterward through the normal scanner-gated service. 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"` keeps the suggestion nudge, `"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).

Autonomous capture in propose and auto modes recognizes prospective rules (for example, “from now on”) and reactive corrections (for example, “that’s not what I asked”). It groups new instructions by topic into up to three proposals per turn, routes vocabulary matches to existing writable workspace skills, and revises its own pending proposal when another correction targets the same skill. For successful substantial work without an explicit correction, an isolated run of the selected model decides whether the completed trajectory clears the conservative 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 the resulting pending proposal only after the isolated run completes. 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.

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

Overview

Plugins

Plugin guides

Building plugins

Skills

Automation

Tools

Agent coordination

How it works

Lifecycle

Lifecycle curation

Chat

Learn from recent work

CLI

Plugin evaluation and lifecycle hooks

Proposal content

Support files

Agent tool

Suggested skills

Scan past sessions

Approval and autonomy

Gateway methods

Storage

Limits

Troubleshooting

Tool-policy diagnostic

​How it works

​Lifecycle

​Lifecycle curation

​Chat

​Learn from recent work

​CLI

​Plugin evaluation and lifecycle hooks

​Proposal content

​Support files

​Agent tool

​Suggested skills

​Scan past sessions

​Approval and autonomy

​Gateway methods

​Storage

​Limits

​Troubleshooting

​Tool-policy diagnostic

​Related

How it works

Lifecycle

Lifecycle curation

Chat

Learn from recent work

CLI

Plugin evaluation and lifecycle hooks

Proposal content

Support files

Agent tool

Suggested skills

Scan past sessions

Approval and autonomy

Gateway methods

Storage

Limits

Troubleshooting

Tool-policy diagnostic

Related