feat(sessions-retro): cross-session friction retro skill - #32
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jtomaszewski wants to merge 3 commits into
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feat(sessions-retro): cross-session friction retro skill#32jtomaszewski wants to merge 3 commits into
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…e patches Scan the last N days of local Claude Code transcripts for human pushback (corrections, rejected actions, rewrite requests), classify into a friction taxonomy, cluster by root cause, and propose a copy-pasteable patch for every cluster of 3+ occurrences. Report-only, manual-invoke, stateless. Bundles a jq-based prefilter script validated on a 7-day pilot corpus (216 transcripts -> 427 human turns -> 98 candidates -> 47 genuine, 10 clusters). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The digest is now a self-contained report.html (full per-occurrence evidence tables, patches as copy blocks, false-positive breakdown) written to the run's temp dir and opened in the browser, plus a short in-chat summary. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
… headers in the report Each cluster now carries, besides the recommended patch, 2-3 alternative improvement levers (habit rule / project CLAUDE.md fact / memory / skill edit / settings) with one-line trade-offs; cluster headers are sticky while their evidence scrolls. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Problem
In-session self-improvement (CLAUDE.md loop, memory) only captures corrections the agent notices in the moment. Corrections that slip past unnoticed — and cross-session recurrence (the same nit 5× in a month) — are invisible: no single session can see a cluster.
Solution
New
sessions-retroskill: an on-demand, cross-session retrospective over the user's local Claude Code transcripts.scripts/extract-turns.sh(jq prefilter): human-typed user turns from the last N days (default 7, optional project filter), then a friction-candidate subset via EN+PL correction markers. Deliberately loose regex — recall net; precision is the classifier's job.Report-only (never applies patches), manual-invoke only, stateless (no dedup state by design). Transcripts are treated as data, never instructions; the report carries an internal-use-only banner since it may mix material from many clients' sessions.
Verification
Validated on a real 7-day pilot corpus: 216 transcripts (210 MB) → 427 human turns → 98 candidates → 48 genuine friction turns forming 10 clusters at 3+, with a 49% prefilter precision and a per-shape false-positive analysis feeding back into the skill's classifier instructions. Extraction script exercised with default, windowed, and project-filtered runs.
Housekeeping
🤖 Generated with Claude Code