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.