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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.

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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.
  • Workshop-owned updates: creates target the workspace skills/ root; updates are allowed only when an applied Workshop create proposal owns the workspace-relative skill directory. Handwritten and externally installed workspace skills remain read-only.
  • 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.
  • Revision atomic: create and revise flush a complete immutable proposal generation, publish it with an atomic rename, then sync its parent directory where supported before publishing the SQLite record and event together. Process interruption exposes either the complete previous generation or the complete new one.
  • Consistent surfaces: chat, CLI, and Gateway all call the same service.

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Lifecycle

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

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Collection review

In auto mode, the Gateway runs one system-owned cron job per writable workspace each week. The job appears in openclaw cron list and runs every 7 days. Cron owns the cadence; the job is enabled only when skills.workshop.autonomous.mode is auto. The review can only read skills and submit one atomic collection reconciliation listing only changes. It keeps distinct useful skills, rewrites weak ones, consolidates overlap, and drops junk or stale fragments. Choosing auto intentionally authorizes those rewrites and drops without a second approval for Workshop-owned paths only; propose and off do not run collection review. The reviewer reads each skill it intends to change. Unlisted skills stay untouched. Skills without applied Workshop create provenance are read-only; Workshop-owned skills may receive write or drop. A new skill created during collection review is recorded as an automatically applied create proposal, which makes that directory Workshop-owned. Disabled and agent-filtered skills stay untouched. Recorded usage counts and last-used recency are supporting evidence, not an age-based lifecycle: heavy use favors preserving a skill’s procedure, while no recorded use alone never justifies removing it. Skills that predate ownership tracking, including skills that earlier reconcile runs created directly, have no applied create proposal. Skill Workshop intentionally classifies them as user-authored and read-only. It manages only skills it creates and records from now on. Shared workspaces use the union of each agent’s allowed skills only when provider, model, and resolved auth identity match. Reconciliation must leave every sharing agent at least one visible skill. OpenClaw validates and scans every write before changing the workspace, serializes collection edits with a workspace lease, and retains one backup under the state directory. The changed collection appears in new agent runs; running sessions keep their existing skill snapshot. To undo the last completed cleanup, ask the agent to restore the skill collection. It uses skill_workshop action restore_collection under the same workspace lock. Restore refuses if any affected skill changed after cleanup. Each attempt is persisted per workspace before the model starts. Review is admitted only for collections of at most 200 skills and 240,000 total SKILL.md bytes. Larger collections stay unchanged. The reconciled result must stay inside the same byte limit. Every completed review records its kept, written, and dropped skill names in the shared state database, including the reason for each drop. OpenClaw retains the latest 90 outcomes per workspace. Collection rewrites and merges produce SKILL.md files at or below 10,000 characters. A skill already above the cap can only become shorter. User-authored skills stay untouched.

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Chat

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

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Learn from recent work

Use /learn to route the current conversation or named sources into the best matching pending proposal or live skill, creating a skill only when needed:
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 to revise a matching pending proposal, update a matching live skill, or create a proposal when neither exists. The resulting proposal stays pending; /learn never applies it. Review and apply it through the normal approval flow or with openclaw skills workshop. Create:
Update an existing workspace skill:
If a skill used in the current turn proves wrong or incomplete, the agent reads the live skill and creates a targeted patch proposal. When the complete skill does not fit the selected model’s read budget, the agent can prepare one unique exact span and review its bounded surrounding context before patching it. 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:
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.

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CLI

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.

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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.

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Proposal content

While pending, the proposal is stored as PROPOSAL.md with proposal-only frontmatter:
On apply, Skill Workshop writes the active SKILL.md and removes the proposal-only fields: status, proposal version, and proposal date.

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Support files

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

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Agent tool

The model uses skill_workshop with one required action: create | read | prepare_patch | patch | update | revise | list | inspect | evaluate | apply | reject | quarantine | history | restore_collection. Other parameters apply depending on the action: Only one prepared patch span may be active per skill. A second prepare_patch is rejected until a patch attempt consumes or invalidates the active authorization. inspect returns proposal metadata, a bounded artifact manifest, and one complete artifact when it fits the selected model’s context budget. It selects PROPOSAL.md by default. Set artifact_path to read one support file separately. When the selected artifact does not fit, the result omits its body, reports the original size, and points to smaller per-artifact reads or the unbounded operator CLI command shown above. 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.

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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.

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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 review one finished substantial turn after the agent system becomes idle. The review continues the foreground request prefix, so the provider can reuse its prompt cache. Review transcript and session metadata changes stay detached. It can draft one pending create, patch, or update. In auto mode, creates and Workshop-authored updates use the scanner-gated apply path. User-authored updates stay pending for operator review. A failed review is logged and dropped after one attempt. See Self-learning for enablement, eligibility, privacy and cost details, the proposal threshold, and troubleshooting.

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Approval and autonomy

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. The reviewer may read or prepare an exact span before its single mutation. Existing-skill changes require a complete read receipt or prepared exact-span authority, plus 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.

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Gateway methods

skills.curator.status reports live skill usage recorded from trusted skill.used events, plus the latest collection and experience review outcomes per workspace. Age-based skill lifecycle curation is retired. skills.curator.pin, skills.curator.unpin, and skills.curator.restore remain registered for existing clients, but always return an error explaining that the weekly collection review now manages the skill collection. 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.

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Storage

Default state directory: ~/.openclaw.
  • state/openclaw.sqlite: canonical proposal records and provenance, the active generation reference, proposal status, recorded skill usage, collection and experience review outcomes, and apply rollback metadata.
  • Each generation contains one PROPOSAL.md and all of that revision’s support files. Revision publication never overwrites the active generation in place.
  • Generation files are flushed before publication. After the complete bundle is renamed into place, OpenClaw syncs the generations/ parent directory where the platform supports directory flushing, before committing SQLite state. Platforms that report directory synchronization as unsupported retain atomic rename and process-interruption safety, but do not claim power-loss durability for that directory entry.
  • Support files remain beside their generation’s PROPOSAL.md so operators can review the proposed skill as a normal directory.
Proposals created by older releases can still reference the earlier root-level PROPOSAL.md layout. The stored record identifies that bundle directly; the next successful revision moves the proposal onto the generation layout and retires the previous bundle. 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.

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Limits

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Troubleshooting

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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.