Chat and CLI authoring

Chat

For Workshop authoring, ask the agent for the skill you want; it calls skill_workshop and returns a proposal id. Personal library authoring instead returns the managed publication receipt described in Personal library authoring.

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:

/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 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.

When the actual turn supports only personal publication, including paired-node personal CLI authoring, /learn stops without changing a skill. Ask normally for explicit personal creation if you want to publish a revision, or use the existing administrator UI or CLI for Workshop proposal review. Personal pending drafts are not currently supported.

Create:

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

Update an existing Workshop-generated 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. 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:

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

# Createopenclaw skills workshop propose-create \  --name morning-catchup \  --description "Daily inbox catch-up: triage, archive, surface, draft, plan" \  --proposal ./PROPOSAL.md # Update an existing Workshop-generated skillopenclaw skills workshop propose-update trip-planning --proposal ./PROPOSAL.md # List and inspectopenclaw skills workshop listopenclaw skills workshop inspect <proposal-id> # Revise before approvalopenclaw skills workshop revise <proposal-id> --proposal ./PROPOSAL.md # Run installed plugin evaluators against the exact current draftopenclaw skills workshop evaluate <proposal-id> # Close outopenclaw 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> (agent context; defaults to cwd-inferred, then the default agent) and --json (structured output). Proposals and generated skill targets are scoped to the selected agent. 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.