skill-miner

Two jobs: (1) find where the agent actually suffers in real session logs, (2) find the best existing or drafted skill for each pain point. Never adopt anything here - that is skill-eval-loop's job, with proof.

Mine corrections first (where skills win)

python3 scripts/corrections.py --memory ~/.claude/projects/<proj>/memory --out corrections.json

Correction episodes are the moments the user told the agent it got something wrong: (agent message, user reply). An LLM groups them into themes, marks each one knowledge_gap (fixing it needs THIS user's rules, voice, formats or facts) vs generic, and maps it to the user's saved rule files (--memory). Knowledge-gap themes sort first. They are what a skill can measurably fix: frontier models already avoid generic mistakes, so generic-failure evals tie, while a skill carrying the user's rules wins.