distill-feedback
distill-feedback is a Session review & learning skill for AI agents, published by AnastasiyaW in the claude-code-config repository on GitHub. Skillspace rates distill-feedback C (74/100) and it passed the safety scan; the rating is based on static analysis only.
Turn captured user-correction signals into durable rules (learn-from-corrections loop). Use when - /distill-feedback, "process feedback queue", "what corrections did I give you", "encode lessons from my corrections", session-feedback-capture queued sessions, "обнови правила по моим поправкам", "разбери очередь обратной связи". Reads ~/.claude/feedback/queue.jsonl, LLM-semantically detects durable corrections, proposes atomic rules, applies human-gated via delta-merge. Do NOT use to act on a single in-session correction (just apply the fix directly) or to hand-edit settings.json behaviors; this only mines the queued feedback backlog into durable rules.
- Grade
- C
- Quality
- 74 / 100
- Safety
- Passed
- GitHub stars
- 135
Unproven: rated by static analysis only, not yet compared head-to-head.
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Our review
- Architecture
- 100 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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