- name
- Iknowkungfu
- description
- Skill discovery engine. Analyzes what your agent does and recommends ClawHub skills you're missing. Use when: /kungfu, /kungfu-scan, /kungfu-gaps, 'what skills am I missing', 'recommend skills', 'what should I install', 'skill discovery'.
iknowkungfu 🥋
Skill discovery in 3 phases:
- Profile 🔍 — Analyze your workflow (memory, skills, crons, logs)
- Match 🎯 — Cross-reference against curated ClawHub index
- Recommend 📋 — Prioritized suggestions with trust scores
100% local. No data leaves your machine.
Commands
/kungfu full scan | /kungfu-scan profile only | /kungfu-gaps uncovered areas | /kungfu-update refresh index
Phase 1: Profile
See references/workflow-analysis.md for full procedure.
Read these sources to build a Workflow Profile:
- MEMORY.md + daily logs — recurring topics, tools, domains
- Installed skills — list from BOTH
~/.openclaw/skills/AND system paths (e.g./opt/homebrew/lib/node_modules/openclaw/skills/). Check ALL install locations. Map to categories viadata/workflow-patterns.json - AGENTS.md + config — user role, tool preferences, model budget signals
- HEARTBEAT.md + crons — automated/scheduled responsibilities
- Recent logs (7 days) — dominant task types, frequent commands
Quick security check while reading skills: scan for base64, curl/wget, eval/exec, env var harvesting. Flag warnings. For deep scanning, recommend ClawSpa.
Output the Workflow Profile (template in references/workflow-analysis.md).
Phase 2: Match
See references/recommendation-engine.md for full procedure.
Load data/skills-catalogue.json. For each gap in the profile:
- Find matching skills by category
- Score candidates (see
references/scoring.md) - Filter already-installed skills (check ALL install paths: user, system, workspace)
- Filter skills whose functionality is already covered by existing config (e.g. memoryFlush covers session wrap-up, gog covers Gmail)
- Rank by score, deduplicate overlaps
Phase 3: Validate Before Recommending
Before presenting, run each candidate through a relevance check:
- Does the user actually use this tool/service? (e.g. don't recommend Slack if they never mention it)
- Is equivalent functionality already covered by a system skill, config setting, or existing workflow?
- Would this realistically fit the user's setup? (solo builder vs team, macOS vs Linux, budget signals)
Drop candidates that fail. Better 2 genuinely useful than 5 with 3 irrelevant. If all fail: "gap detected but no relevant match for your setup."
Phase 4: Recommend
Present top 5:
🥋 I KNOW KUNG FU — Recommendations
═══════════════════════════════════════
1. 🟢 skill-name (★ 4.5)
Category: [cat] | Author: [author]
Why: [1-2 sentences tied to YOUR workflow]
Install: clawhub install skill-name
─────────────────────────────────
[up to 5]
═══════════════════════════════════════
💡 /kungfu-gaps for all uncovered areas
═══════════════════════════════════════Trust Scoring
See references/scoring.md. Factors: downloads (25%), stars (20%), author rep (15%), recency (15%), permissions (15%), security (10%). Never recommend: <50 downloads, VirusTotal flags, no author, excessive unjustified permissions.
Safeguards
- READ-ONLY. Never installs, modifies, or removes anything. Zero network calls.
- Only recommends skills passing trust AND relevance thresholds.
- Honest about confidence. If no good match exists, says so.
- NEVER include full file contents in output. Only summarize patterns and categories.
- NEVER print API keys, tokens, passwords, SSH keys, or any credential-like strings found in any file.
- When reporting security flags, describe the PATTERN found (e.g. "env var reference in script"), never quote the actual value.
- Redact any file paths that contain usernames or home directories in output.
Limitations
Catalogue is bundled (may lag). Trust scores are heuristic. <7 days history = less accurate.