AnastasiyaW 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2026-09-06; its F1 claims are withdrawn from this skill until the dataset, labels, model,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 studies evolving context;
TRACE studies compiling corrections into runtime checks.
Neither proves this local extractor's accuracy. The approval boundary comes from our
applicable user instructions and autonomy-risk-tiers.md, not an inferred paper mandate.
New standing rules are proposals; an already authorized correction to an existing rule
can be implemented within that exact authority. Codex memory changes also require the
separate explicit user request and supported memory-update channel.
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. Resolve authority for the exact change
Show the user a compact table: each proposed rule + its applicability condition + source quote +
target file + action (ADD new / EDIT existing / SUPERSEDE old / SPLIT). If that exact change
is not already authorized, ask for approval and retain the proposal. Do not repeatedly
request permission already given for the same in-scope correction. New always-on rules,
SUPERSEDE and DELETE require the applicable explicit authority.
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 only reconciled sessions processed
First account for every selected queue item: inspected transcript, accepted/rejected
corrections and their resulting artifact, or an explicit unresolved evidence gap.
The extractor's pending is the selected window when --limit is used, not whole-queue
completion. A missing/unreadable transcript is not processed; locate the canonical
private chat archive by session id and inspect the recovered transcript before closing it.
Only ids actually reconciled in this pass go into the following command:
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
- Missing path is not lost history. The extractor omits empty/unreadable sessions from its payload. Reconcile the selected queue ids against payload ids, search the existing private archive and leave unrecovered ids pending with the observed failure. Never mark an omitted id processed merely to silence the nudge.
- Confidence is not authority. Apply step 4 using the current user's actual authorization.
- 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, payload looks empty → compare queue and payload ids; recover missing transcripts from the private archive. A skipped payload is not evidence of completed work.
- Extractor prints
pending: Nbutsessions: []→ all N transcripts are missing/unreadable; diagnose/recover those specific ids and retain unresolved ones; do not mark them processed. - Want to pause capture entirely →
touch ~/.claude/.skip-feedback-capture(orCLAUDE_SKIP_FEEDBACK_CAPTURE=1); the Stop hook then no-ops.
Related
rules/learn-from-corrections.md— the protocol + the evidence behind LLM-semantic + human-gaterules/memory-maintenance.md— the delta-merge (ACE) discipline step 5 reuseshooks/session-feedback-capture.py(Stop, capture) ·hooks/feedback-pending-show.py(SessionStart, nudge)