Skill Forge

Detect repeated capability gaps, convert recurring user needs into candidate skills, scaffold new OpenClaw-compatible skills, and validate them before installation. Use when an agent repeatedly fails the same task, sees recurring feature requests, notices repetitive manual workflows, or wants to propose a new skill instead of solving the same problem ad hoc.

Use this skill to turn repeated demand into a reviewed skill candidate.

Current version: v1.4.0 "Evolve First".

Core jobs

1. Detect repeated capability gaps from logs, .learnings, and feature requests.
2. Decide whether the pattern is stable enough to deserve its own skill.
3. Classify the opportunity as academic, product, integration, script, or workflow.
4. Generate a candidate skill folder with a usable SKILL.md, agents/openai.yaml, and profile-specific resources.
5. Validate and score the candidate before proposing installation.
6. Run hidden smoke evaluation and Agent profile authorization before confirmed installation.
7. Record feedback from later usage and propose reviewed updates instead of mutating installed skills directly.
8. Provide the Somnia runtime used by scheduled nightly review to find skills with bugs, weak scores, or update-worthy feedback.
9. Use redacted replay cases to check whether candidates actually cover real feedback-derived tasks.
10. Provide a product console (scripts/skill_forge.py) so agents can run doctor checks, demos, forge flows, evolution, feedback capture, replay, and release gates from one entry point.

Trigger Cues

Use this skill when the user or agent mentions:

• repeated failure
• missing capability
• recurring workflow
• feature request
• make a new skill
• scaffold a skill
• validate a generated skill

Default Workflow

1. Detect repeated capability gaps from learning files or session-derived notes.
2. Classify the strongest opportunity into a skill profile.
3. When --prefer-evolve is set, route the opportunity through decide_skill_action.py first; if it matches an installed skill, evolve that skill instead of forging a new one.
4. Scaffold a candidate skill with profile-specific resources (forge path).
5. Validate the candidate and inspect score, warnings, and references.
6. Run hidden smoke evaluation without exposing simulated cases to users.
7. Propose installation, then require Telegram approval before applying it.
8. Record future usage feedback and run an evolution pipeline when enough feedback accumulates.
9. During scheduled reviews, write summary reports and optionally propose updates without exposing hidden evaluation details.
10. Run replay evaluation before approving evolved candidates when replay cases exist.
11. Before release, run package checks and golden scaffold checks across all supported profiles.
12. Run deterministic benchmarks before release to enforce validation, hidden-eval, line-count, and runtime budgets.

Resource Loading

Keep this SKILL.md focused on the operating path. Load bundled references only when the current task needs them:

• Read references/heuristics.md when deciding whether a repeated need deserves a skill.
• Read references/skill-quality-rubric.md when reviewing candidate quality or explaining a validation score.
• Read references/milestone-architecture.md when changing install gates, replay gates, or evolution flow.
• Read references/forge-console.md when using or modifying the product console.

Execution Mode

Use medium freedom for planning and candidate design, but low freedom for install, uninstall, release checks, signed approval, replay regression, and file mutation paths.

Commands

Forge Console

Prefer the product console for normal use:

bash

python3 {baseDir}/scripts/skill_forge.py doctor --json
python3 {baseDir}/scripts/skill_forge.py demo --json
python3 {baseDir}/scripts/skill_forge.py forge --output ./generated --install plan --json
python3 {baseDir}/scripts/skill_forge.py release-check --json

From the source repository, make package-check runs the same release gate plus secret scanning.

Use lower-level scripts only when a workflow needs exact control over one stage.

Detect

Run:

bash

python3 {baseDir}/scripts/detect_skill_opportunities.py --json

Add --source paths when a specific workspace or learnings file should be analyzed.

Decide (evolve-or-forge routing)

After detect, ask whether the opportunity should evolve an existing installed skill or forge a new one. Use this to prevent skill sprawl when a request is really just a new trigger surface for something the user already installed.

bash

python3 {baseDir}/scripts/skill_forge.py decide \
  --skill-name "literature-review-helper" \
  --triggers "APA format, MLA format, literature review, source metadata" \
  --template academic \
  --reason "user keeps asking for APA literature reviews" \
  --json

Output is one of evolve / forge / ambiguous plus per-candidate fitness scores. To run the full pipeline with auto-routing, pass --prefer-evolve to forge:

bash

python3 {baseDir}/scripts/skill_forge.py forge \
  --output ./generated --install plan --prefer-evolve --json

Profile mismatch is a hard gate (fitness 0). The replay non-regression gate downstream catches any mis-routed evolution that would degrade the installed baseline.

Choose

Only create a skill when the pattern is:

• recurring
• broad enough to reuse
• structured enough to document
• more stable than a one-off prompt

If the need is too narrow, keep it as a note or workflow rule instead.

Scaffold

Run:

bash

python3 {baseDir}/scripts/generate_skill_scaffold.py \
  --skill-name my-skill \
  --output ./generated \
  --goal "What the skill should achieve." \
  --triggers "keyword1, keyword2" \
  --template auto

Validate

Run:

bash

python3 {baseDir}/scripts/validate_skill_candidate.py ./generated/my-skill

Propose Installation

Plan only (safe to run anywhere, no Telegram, no secrets):

bash

python3 {baseDir}/scripts/propose_skill_install.py ./generated/my-skill --json

--apply is gated by a signed approval token. Direct invocation without a token is blocked. Use the full pipeline (forge_pipeline.py --install telegram) for the normal install path, or call telegram_approval.py first and pass its approval_token to propose_skill_install.py --apply --approval-token <token>. The token is HMAC-bound to the skill name, target path, install method, source path, and a hash of the source directory's contents, so any post-approval edit invalidates it. Token issuance and install apply both require the source to be an existing skill directory containing SKILL.md.

Record Feedback

Run:

bash

python3 {baseDir}/scripts/record_skill_feedback.py \
  --skill my-skill \
  --agent-name StudyAgent \
  --rating negative \
  --feedback "触发词: literature review. The skill should handle literature review planning better." \
  --json

Propose Evolution

Run:

bash

python3 {baseDir}/scripts/evolve_skill_pipeline.py \
  --skill my-skill \
  --output ./generated-updates \
  --install plan \
  --replay hidden \
  --min-replay-improvement 0 \
  --agent-name StudyAgent \
  --json

When an installed copy of my-skill exists under --target-root, the candidate and the installed baseline are scored against the same redacted replay cases. The pipeline blocks the install when candidate_score - baseline_score < --min-replay-improvement (default 0, i.e. no regression). Pass --min-replay-improvement -100 to disable the regression check.

Replay Evaluation

Run:

bash

python3 {baseDir}/scripts/replay/collect_replay_cases.py \
  --feedback-file ~/.openclaw/workspace/.learnings/skill-feedback.jsonl \
  --skill my-skill \
  --json

python3 {baseDir}/scripts/replay/run_replay_eval.py \
  --skill-dir ./generated-updates/my-skill \
  --skill my-skill \
  --json

Nightly Review

Somnia is now packaged as its own skill for scheduled maintenance. These compatibility commands remain available from Skill Forge:

bash

python3 {baseDir}/scripts/nightly_skill_review.py \
  --scope managed \
  --propose-updates \
  --replay hidden \
  --update-install plan \
  --json

Install a macOS daily sleep-hour schedule, defaulting to 03:00 local time:

bash

python3 {baseDir}/scripts/schedule_nightly_review.py \
  --hour 3 \
  --minute 0 \
  --scope managed \
  --propose-updates \
  --update-install plan \
  --apply \
  --json

Use --telegram-report with --env-file ~/.openclaw/skill-forge.env when scheduled runs should send a summary report to Telegram. The env file should define TELEGRAM_BOT_TOKEN and TELEGRAM_CHAT_ID.

Uninstall or rollback a generated skill. Like install, --uninstall --apply requires a signed approval token issued by telegram_approval.py for the same skill, target, and method. Plan-only (no --apply) works without a token and just prints what would happen:

bash

# Plan (no token, no mutation)
python3 {baseDir}/scripts/propose_skill_install.py my-skill --uninstall --json

# Apply (requires a fresh approval token bound to the same skill/target/method)
python3 {baseDir}/scripts/install/telegram_approval.py \
  --skill my-skill \
  --profile workflow \
  --validation-score 100 --validation-grade milestone \
  --evaluation-score 100 --evaluation-passed \
  --target "$HOME/.openclaw/workspace/skills/my-skill" \
  --method symlink \
  --json > approval.json

APPROVAL_TOKEN="$(python3 -c 'import json; print(json.load(open("approval.json"))["approval_token"])')"

python3 {baseDir}/scripts/propose_skill_install.py my-skill \
  --uninstall --apply \
  --approval-token "$APPROVAL_TOKEN" \
  --restore-backup \
  --json

Full Pipeline

For one-shot operation:

bash

python3 {baseDir}/scripts/forge_pipeline.py \
  --source ~/.openclaw/workspace/.learnings/FEATURE_REQUESTS.md \
  --source ~/.openclaw/workspace/.learnings/ERRORS.md \
  --output ./generated \
  --eval hidden \
  --json

Ask through Telegram before installing:

bash

export TELEGRAM_BOT_TOKEN="..."
export TELEGRAM_CHAT_ID="..."

python3 {baseDir}/scripts/forge_pipeline.py \
  --source ~/.openclaw/workspace/.learnings/FEATURE_REQUESTS.md \
  --source ~/.openclaw/workspace/.learnings/ERRORS.md \
  --output ./generated \
  --install telegram \
  --agent-name StudyAgent \
  --json

Decision rules

• Prefer creating a skill over a new agent when the new capability is narrow.
• Prefer a new agent over a skill when the work needs a distinct role, tools, and long-term memory boundary.
• Prefer references/ when the skill mainly teaches structure and judgment.
• Prefer scripts/ when the same code would otherwise be rewritten repeatedly.
• Default to --install plan; use --install telegram for any mutation.
• Treat --install ask and --install auto as blocked compatibility aliases.
• Do not call propose_skill_install.py --apply directly; --apply requires a signed approval token issued by telegram_approval.py and bound to the skill name, target, method, source path, and source content hash. The HMAC secret is SKILL_FORGE_APPROVAL_SECRET if set, otherwise derived from the bot token. Tokens default to a 30-minute TTL.
• A dry-run approval token (mode=dry-run) cannot mutate state. The pipeline reports install_status=dry-run-blocked and only --allow-dry-run-install on the install script lets it through, in which case the audit field is approved_by=dry-run.
• Keep --eval hidden for user-facing flows so simulated checks and prompts are not exposed.
• Use --agent-name before installation when a specific agent will receive the skill.
• Never hard-code Telegram tokens; discover them from OpenClaw/env files or environment variables.
• Redact feedback text before storing it.
• Treat feedback-driven changes as update candidates, not direct edits to installed skills.
• Install evolved skills only after validation, hidden evaluation, authorization, and approval.
• Replay runs as a non-regression gate during evolution: baseline (currently installed) and candidate are scored against the same redacted cases, and the candidate is blocked if score - baseline < --min-replay-improvement (default 0). Use --min-replay-improvement -100 to disable. When no installed baseline exists the gate falls back to a single-skill coverage check.
• Scheduled nightly review may propose updates, but install changes still require Telegram approval.
• Nightly reports should show only health summaries, not hidden evaluation prompts or simulated checks.
• Default nightly review scope is managed; use --scope all only for explicit full inventory audits.

Output Contract

The skill-forge output should include:

• detected opportunity name
• recommended template profile
• generated candidate path
• validation score and grade
• install status
• approval status when Telegram confirmation is used
• nightly review report path when running scheduled review
• install plan
• review warnings, if any

Quality Gates

Before proposing installation, confirm:

• the skill name is concrete and reusable
• the description has clear trigger conditions
• the generated structure matches the intended job
• the candidate improves a real recurring workflow
• validation score is at least 70
• grade=milestone is preferred before sharing externally
• installation threshold is at least 85 unless the user explicitly chooses otherwise
• hidden smoke evaluation passes
• target Agent profile policy allows the generated skill profile
• Telegram approval is available before applying install or uninstall changes
• feedback-derived updates are reviewed as candidates before replacing an installed skill
• replay evaluation passes when replay cases exist
• nightly review reports are written before any update proposal is installed
• scheduled automation uses explicit launchd configuration and can be uninstalled

Read references/skill-quality-rubric.md when evaluating a draft.

Resources

References:

• references/heuristics.md
• references/skill-quality-rubric.md
• references/milestone-architecture.md
• references/forge-console.md

Scripts:

• scripts/detect_skill_opportunities.py
• scripts/evaluate_skill_candidate.py
• scripts/generate_skill_scaffold.py
• scripts/validate_skill_candidate.py
• scripts/forge_pipeline.py
• scripts/skill_forge.py
• scripts/install/propose_skill_install.py
• scripts/install/telegram_approval.py
• scripts/evolve/evolve_skill_pipeline.py
• scripts/evolve/propose_skill_update.py
• scripts/evolve/record_skill_feedback.py
• scripts/replay/collect_replay_cases.py
• scripts/replay/compare_replay_outputs.py
• scripts/replay/redact_replay_case.py
• scripts/replay/replay_report.py
• scripts/replay/run_replay_eval.py
• scripts/somnia/nightly_skill_review.py
• scripts/somnia/schedule_nightly_review.py

Compatibility wrappers:

• scripts/propose_skill_install.py
• scripts/telegram_approval.py
• scripts/evolve_skill_pipeline.py
• scripts/propose_skill_update.py
• scripts/record_skill_feedback.py
• scripts/nightly_skill_review.py
• scripts/schedule_nightly_review.py