Iknowkungfu

# iknowkungfu 🥋 Skill discovery in 3 phases: 1. **Profile** 🔍 — Analyze your workflow (memory, skills, crons, logs) 2. **Match** 🎯 — Cross-reference against curated ClawHub index 3. **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 via `data/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: 1. Find matching skills by category 2. Score candidates (see `references/scoring.md`) 3. Filter already-installed skills (check ALL install paths: user, system, workspace) 4. Filter skills whose functionality is already covered by existing config (e.g. memoryFlush covers session wrap-up, gog covers Gmail) 5. 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.