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Split failure reflections into SKILL_DEFECT (body edit) vs EXECUTION_LAPSE (protected appendix note that re-emphasizes an existing rule, never edited by step-level analysts). Toggle: optimizer.use_skill_aware_reflection (default false; baseline byte-identical when off). - optimizer/appendix.py: protected APPENDIX region (inject/extract/append with dedup), mirrors the slow_update protected-field pattern - optimizer/skill_aware.py: analyst prompt augmentation, appendix_notes parsing, threshold-gated LLM consolidation, and a process-wide runtime switch (configure_skill_aware_reflection) set once by the trainer - gradient/reflect.py: augment error/success analyst prompts at runtime; None-sentinel kwargs resolve from the global switch, so env adapters need no per-benchmark wiring (works for all envs, present and future) - optimizer/skill.py: generalize the protected-region check to (slow_update, appendix); edits inside any protected region are skipped - engine/trainer.py: inject appendix at init, flush per-step EXECUTION_LAPSE notes after the gate settles, optional consolidation - tests: regression suite incl. toggle-off byte-identical guarantee and env-independent global-switch resolution (6/6 passing + live smoke) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>