review-skills

name	review-skills
description	Review skills that the skill-extractor mined from your Claude Code traces — inspect each candidate's definition and confidence/utility scores, edit it, then install it as a real skill or reject it with a comment. Use when the user runs /review-skills, asks to "review discovered/mined skills", or a session-start banner reports pending skills.

Review discovered skills

The skill-extractor mines reusable procedures ("skills") from Claude Code conversation + coding traces, scores each by confidence (is this a real, generalizable skill?) and utility (how valuable if reused?), and queues the strong, novel ones for human review. This flow lets the user approve, edit, or reject them. Every candidate — approved or not — stays on record so mining improves over time.

The engine lives at ~/.claude/skill-extractor/. Use the review CLI for all actions (never hand-edit the state files). Resolve the paths first:

PY=$(cat ~/.claude/skill-extractor/extension-config.json | python3 -c "import json,sys;print(json.load(sys.stdin)['python'])")
ENGINE=$(cat ~/.claude/skill-extractor/extension-config.json | python3 -c "import json,sys;print(json.load(sys.stdin)['engineDir'])")

(Fallback: PY=python3, ENGINE=~/Nesh/skill-extractor/engine.)

Steps

1. List pending candidates: run "$PY" "$ENGINE/review.py" list. This returns JSON with id, name, title, description, confidence, utility, composite, and trace_outcome for each. If empty, tell the user there's nothing to review and optionally offer to run the miner now ("$PY" "$ENGINE/extractor.py").

2. Present them to the user as a concise ranked list (highest composite first): title, one-line description, and the scores like confidence 88% · utility 77% · trace: success. Briefly note what each score means the first time.

3. For the one(s) the user wants to look at, run "$PY" "$ENGINE/review.py" show <id> and show the full body (the procedure), trigger, and outcome_reason.

4. Take the user's decision for each candidate:

   • Install (optionally with edits): if the user wants changes, write a JSON file with only the changed fields (any of name, title, description, trigger, body, tags) to a temp path, then run "$PY" "$ENGINE/review.py" install <id> --edits /tmp/edits.json --comment "<why>". With no edits, drop --edits. This writes ~/.claude/skills/<name>/SKILL.md so it becomes a live skill.
   • Reject: run "$PY" "$ENGINE/review.py" reject <id> --comment "<why they passed>". Always try to capture a short reason — it's the training signal for future mining. The candidate stays in the scratch store; it is not deleted.

5. Confirm what happened (installed path, or rejection recorded) and, if the user installed a skill, remind them it's available immediately in new sessions.

Notes

• Never delete candidates. Reject keeps them; install promotes them. This is by design.
• If the user asks to see everything mined (not just pending), use list --all.
• The same actions are available via the VS Code popup ("Discovered Skills" panel); this command is the terminal equivalent and stays in sync with it.