distill-feedback

Agent Building

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.

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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 LLM-semantic, not keywords: we independently tested a keyword detector. It scored F1 0.42 on held-out cases and missed ~60% of real corrections (every keyword-free one, e.g. "в следующий раз лучше через python"). An LLM applying the rubric below scored F1 0.97 on the same set. So detection is semantic. Evidence: knowledge/agent-systems/self-learning-agents/effectiveness-test/RESULTS.md (in the private hub).

Why human-gated: ACE (arXiv 2510.04618) and TRACE (arXiv 2606.13174) both warn that a noisy extractor poisons the rule set. Altering durable rules is also above the auto-act line (autonomy-risk-tiers.md). So this skill proposes; the user approves before anything is written.

Procedure

1. Extract the queue (deterministic)

python ~/.claude/skills/distill-feedback/scripts/extract_feedback_queue.py --limit 8

Returns JSON: {pending, sessions:[{session_id, cwd, ts, user_turns:[...]}]}. --limit bounds the LLM pass (billing: distillation is opt-in, not every-session). If pending is 0, stop — nothing to do.

2. Detect durable corrections (LLM-semantic, prefer a fresh sub-agent)

For independence (Generator-Evaluator), spawn a sub-agent with the rubric below and the extracted user_turns. Ask it to return, per genuine correction: {quote, durable_rule, applicability_condition, confidence, session_id}. Pass only the turns — not your own reasoning.

RUBRIC — a user turn is a DURABLE CORRECTION if the user pushes back on / redirects the agent's behavior in a way that implies a STANDING preference or a mistake to avoid in future:

explicit pushback / redirection ("no, do X instead", "wrong file again")
reminder of a prior agreement ("we agreed you'd ask first", "мы же договаривались сначала бэкап")
standing-preference marker ("from now on / always / never / by default / в следующий раз / впредь")
frustration at a REPEATED mistake ("опять", "again", "you keep")
polite redirection phrased as a question ("could you not overwrite latest.pth each time?")
revert with a reason ("верни как было, твоя версия хуже")
praise THEN correction — judge the whole turn ("great it runs, but always pin versions" = YES)

NOT a durable correction: new feature/task request · diagnostic question ("why did the build fail?", "почему-то падает") · factual/info statement even with "should be / by default / never" ("deploy should be done in 5 min", "по умолчанию там 8080") · agreement ("actually that makes sense, go ahead") · reassurance ("don't worry about the tests") · praise-only · off-topic chatter.

3. Dedup + draft atomic rules

For each detected correction: write it as ONE atomic rule with an applicability condition. Dedup against existing rules/memory (grep ~/.claude/rules/ and the project memory) — if it is already a rule, skip or propose an EDIT, not a new ADD. Cluster duplicates across sessions into one rule.

4. Propose (human-gate — MANDATORY)

Show the user a compact table: each proposed rule + its applicability condition + source quote + target file + action (ADD new / EDIT existing / SUPERSEDE old / SPLIT). Ask for approval. Do NOT write anything yet. SUPERSEDE/DELETE always need explicit confirmation.

5. Apply (delta-merge, never rewrite)

On approval, apply each accepted delta with the ACE discipline from memory-maintenance.md: addressable ADD/EDIT only, dedup, preserve nuance (no full-file rewrite). Put it in the right home (file-organization-cohesion.md): a global rule → ~/.claude/rules/, a project-specific lesson → that project's memory/CLAUDE.md. If the rule is mechanically checkable (file-name shape, forbidden command, tool-call form), note that it should graduate to a hook/validator (deterministic tier beats prose — learn-from-corrections.md).

6. Mark processed

python ~/.claude/skills/distill-feedback/scripts/extract_feedback_queue.py --mark-processed <session_id> ...

Appends to processed.jsonl (append-only; the queue is never rewritten). The SessionStart nudge count drops accordingly.

Gotchas

Transcript may be gone. If a queued session's transcript_path no longer exists, the extractor yields no turns for it — mark it processed and move on (the lesson is lost; nothing to recover).
Don't auto-apply. Even high-confidence corrections go through step 4. A wrong rule is worse than a missed one (it fires on every future session).
Praise-then-correction is the #1 miss. "Спасибо, но впредь не трогай прод" IS a correction. The rubric handles it; don't let a praise-detector suppress it (that bug killed the keyword version).
Billing. Distillation runs an LLM over user turns. Use --limit, run it on-demand (not a hook), and prefer a cheaper model for the detection sub-agent (the rubric is not hard reasoning).
One-off ≠ durable. "переделай, я имел в виду src не dist" is a one-off fix, not a standing rule — the rubric's confidence + your judgment should drop these; only encode what generalizes.

Troubleshooting

Nudge keeps showing, queue looks empty → entries whose transcript vanished are still pending; run the extractor, mark the dead ones processed.
Extractor prints pending: N but sessions: [] → all N transcripts are missing/unreadable; mark them processed.
Want to pause capture entirely → touch ~/.claude/.skip-feedback-capture (or CLAUDE_SKIP_FEEDBACK_CAPTURE=1); the Stop hook then no-ops.

rules/learn-from-corrections.md — the protocol + the evidence behind LLM-semantic + human-gate
rules/memory-maintenance.md — the delta-merge (ACE) discipline step 5 reuses
hooks/session-feedback-capture.py (Stop, capture) · hooks/feedback-pending-show.py (SessionStart, nudge)