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…description evolution Phase 1 (skill evolution) was non-functional against the installed dspy 3.3.1: - dspy.GEPA(max_steps=...) — param renamed upstream to auto/max_full_evals/ max_metric_calls; also never wired optimizer_model into a reflection_lm - skill_fitness_metric only accepted 3 args; GEPA requires the 5-arg (gold, pred, trace, pred_name, pred_trace) signature — wired it to the existing but unused LLMJudge instead of the crude keyword-overlap heuristic - constraint validation ran against the skill body (frontmatter stripped), so skill_structure could never pass — now validates the reassembled file - most severe: SkillModule stored skill_text as a plain attribute while GEPA only mutates predictor instructions, so the exported "evolved" skill was always byte-identical to baseline despite a real score improvement. Skill text is now the predictor's instructions directly, so it's live-read off the mutated signature. - external_importers' Hermes session reader crashed on any non-UTF8 file under ~/.hermes/sessions/ Verified end to end: github-code-review +22.7% (synthetic), +2.1% (sessiondb, mining real Claude Code/Hermes usage); systematic-debugging -5.8% (Gemini) — a genuine regression, GEPA over-compressed and lost substance. Phase 2 (tool description evolution) didn't exist upstream — built evolution/tools/tool_module.py + evolve_tool_descriptions.py mirroring the Phase 1 pattern: all target tool descriptions optimized together in one router instructions block (cross-tool competition, not independent mutation) using marker-delimited sections that survive GEPA rewrites. First real run (read_file/write_file/search_files/terminal, Grok 4.3) hit 100% baseline accuracy immediately — no learning signal, dataset too easy; needs harder confusable cases to be a meaningful test.
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Note: upstream merge permissions are not available to ideas24h, so this PR is published for maintainer merge.