Chaim
c7156b575e
Make the effort-calibration recommendation style-clean (style acquisition is
the goal — write like chair Dafna Tamir):
- recommend_effort ranks by anti_pattern_total (PRIMARY, the clean style-rule
violation count) → golden_ratio_deviation_pp (SECONDARY, structural-style,
None=worst) → distance (faint final tiebreak). change_percent is EXCLUDED from
the ranking (it mixes style with content-completeness per 07-learning §0.7 —
noise for STYLE) and kept in the report as context only.
- Add a confidence flag ("clear"/"weak"): a pick whose anti-pattern lead over the
runner-up is within noise (margin < epsilon = max(0.5, 0.20·spread)) is flagged
⚠️ weak, so an over-claimed pick (the old confident block-zayin "low") is never
shown as confident. Rendered as a confidence column in the report.
- Add --rerank <report.json>: re-apply the new key to a saved calibration JSON
offline (NO LLM, NO DB), print + write *-reranked.md. Re-ranks today's 8-final
run without regenerating.
- recommend_effort stays a single shared pure function reused by
scripts/calibrate_block_yod.py (G2); both --self-test suites pass.
- Spec note added (07-learning §0.7) + SCRIPTS.md updated.
Invariants: G8 (style-clean empirical ranking), G2 (single shared recommend_effort,
reused by the block-yod harness — no parallel metric path), INV-LRN5 (style-only
signal; change% reported-not-ranked).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>