feat(optimizer): skill-aware reflection (EmbodiSkill S_app), config-controlled and env-independent

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>

configs/_base_/default.yaml

@@ -81,6 +81,9 @@ optimizer:
   slow_update_gate_with_selection: false
   longitudinal_pair_policy: mixed  # mixed / changed / unchanged
   use_meta_skill: true
+  use_skill_aware_reflection: false  # EmbodiSkill: split failures into SKILL_DEFECT (edit body) vs EXECUTION_LAPSE (protected appendix)
+  skill_aware_appendix_source: both  # both = success+failure emit appendix notes; failure_only = only EXECUTION_LAPSE (paper-faithful)
+  skill_aware_consolidate_threshold: 0  # 0 = off; >0 = LLM-consolidate the appendix when its note count exceeds N
 
 evaluation:
   use_gate: true