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distill-feedback

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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.

skills/development/distill-feedback/SKILL.md

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Nov 8, 2026
License
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# distill-feedback — close the learn-from-corrections loop

The Stop hook `session-feedback-capture.py` queues finished sessions into
`~/.claude/feedback/queue.jsonl`. This skill processes that queue: it finds the user turns
that were **durable corrections** of the agent's work and turns them into rules — so the same
correction never has to be given twice.

**Why semantic review:** standing preferences depend on context, not a trigger-word count.
The formerly cited private `effectiveness-test/RESULTS.md` was absent when checked on
2026-09-06; its F1 claims are withdrawn from this skill until the dataset, labels, model,
held-out split and raw predictions can be inspected. The rubric below is a review method,
not a demonstrated accuracy guarantee. Do not substitute another paper's scores for our own.

**Research and authority:** [ACE](https://arxiv.org/abs/2510.04618) studies evolving context;