Skill: omaression/skill-enhancer (version 1.0.0-alpha)

--- name: skill-auditor description: Periodically audit all workspace skills, learnings, memory, and configuration files to recommend refactoring, new skill ideas, and workflow improvements. Triggered automatically via cron every 7 days, or manually with "audit skills", "skill review", "workspace health", or "improve workflow". Sends recommendations directly to Telegram without user prompting. --- # Skill Auditor Automated weekly workspace health check. Evaluates skills, learnings, memory, and config files. Delivers actionable recommendations to Telegram. ## Pipeline architecture 4-phase sequential pipeline with internal parallelism: ### Phase 1: Digest (`opencode-go/kimi-k2.5`) Ingest all workspace files in one long-context call: - `skills/*/SKILL.md` and associated scripts/tests - `.learnings/LEARNINGS.md`, `ERRORS.md`, `FEATURE_REQUESTS.md` - `SOUL.md`, `AGENTS.md`, `USER.md`, `TOOLS.md`, `MEMORY.md`, `HEARTBEAT.md` - recent `memory/*.md` files (last 14 days) Output: `audit-state.json` with per-file summaries, staleness scores, overlap detection, gap analysis. Optimization: hash watched files against `state.json` from last run. Skip unchanged files to prevent token burn. Also: `web_search` for best practices relevant to detected gaps. ### Phase 2: Evaluate (parallel) **Phase 2A** (`opencode-go/glm-5`): Score each skill on effectiveness, token efficiency, coverage, staleness, overlap, alignment with USER.md goals. Propose new skill ideas. **Phase 2B** (`openai-codex/gpt-5.3-codex`): Score independently. Generate concrete refactor proposals. Propose new skill ideas. Both output structured evaluation JSON. ### Phase 3: Judge (`openai-codex/gpt-5.4`) Receives: `audit-state.json` + both evaluation outputs. - Cross-validate proposals, resolve conflicts - Filter: only recommend changes with clear ROI - Classify each recommendation: - 🟢 **safe refactor** — low-risk, can PR directly after approval - 🟡 **needs review** — structural change or new skill creation - 🔴 **informational** — trend or observation, no action yet - Confidence threshold: ≥0.7 to recommend, ≥0.85 for safe-refactor classification Output: `final-recommendations.json` ### Phase 4: Deliver (main session) Format recommendations as Telegram message and send. Archive to `memory/audits/YYYY-MM-DD.json`. ## Recommendation format Each recommendation: ```json { "id": "rec-001", "type": "refactor | new-skill | config-update | deprecate | merge", "severity": "green | yellow | red", "target": "skills/context-optimizer/SKILL.md", "title": "compress context-optimizer references section", "rationale": "...", "proposed_action": "...", "confidence": 0.87, "agreed_by": ["glm-5", "gpt-5.3-codex"] } ``` ## Telegram delivery format ``` 📋 Weekly Skill Audit — YYYY-MM-DD 🟢 Safe refactors (N): 1. [title] → [one-line action] 🟡 Needs review (N): 2. [title] 🔴 Informational (N): 3. [title] Reply with a number for details, or "approve 1,2" to greenlight. ``` If no strong recommendations: send "no action needed this week" one-liner. If quality score is low across all recommendations: send nothing. ## Scheduling **Primary:** OpenClaw cron, every 7 days (Sunday 10:00 AM ET): ``` openclaw cron add --schedule "0 10 * * 0" --model openai-codex/gpt-5.4 --label skill-auditor-weekly --prompt "Read skills/skill-auditor/SKILL.md and execute the full audit pipeline. Deliver results to Telegram." ``` **State tracking:** `memory/audits/last-run.json` records last execution timestamp. Heartbeat checks if last run was >10 days ago and alerts. **Manual trigger:** User says "audit skills" or "review workflow". ## Evaluation criteria Each file/skill scored on: 1. **Effectiveness** — achieves stated purpose? (1-5) 2. **Token cost** — bloated? shorter without losing value? (1-5) 3. **Coverage** — workflow gaps not addressed by any skill? (binary + description) 4. **Freshness** — last meaningful update vs relevance decay 5. **Overlap** — duplicates content in another file/skill? (list pairs) 6. **Alignment** — matches USER.md goals and SOUL.md persona? (1-5) ## Safety rules - No automatic file edits. Recommendations are advisory until approved. - Green recommendations produce diff previews; actual changes require explicit "approve" reply. - Respect all workspace GitHub handling rules — no repo-visible changes without Omar's approval. ## File structure ``` skills/skill-auditor/ ├── SKILL.md ├── scripts/ │ ├── build_audit_state.py │ ├── merge_evaluations.py │ └── format_telegram.py └── tests/ ├── test_build_audit_state.py ├── test_merge_evaluations.py └── test_format_telegram.py ``` Runtime artifacts (not tracked in repo): ``` memory/audits/ ├── last-run.json ├── YYYY-MM-DD.json └── state.json (file hashes for change detection) ``` ## Validation checklist 1. All 3 helper scripts exist and pass unit tests. 2. Dry-run mode completes full pipeline without sending messages. 3. At least one real audit cycle delivers a well-formatted Telegram message. 4. Recommendations are advisory-only (no auto-edits without approval). 5. Unchanged files are skipped via hash comparison. 6. Confidence thresholds are enforced.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)