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>

scripts/train.py

@@ -245,6 +245,10 @@ def parse_args() -> argparse.Namespace:
     p.add_argument("--longitudinal_pair_policy", type=str,
                    choices=["mixed", "changed", "unchanged"])
     p.add_argument("--use_meta_skill", type=_BOOL)
+    p.add_argument("--use_skill_aware_reflection", type=_BOOL)
+    p.add_argument("--skill_aware_appendix_source", type=str,
+                   choices=["both", "failure_only"])
+    p.add_argument("--skill_aware_consolidate_threshold", type=int)
     p.add_argument("--data_path", type=str)
     p.add_argument("--split_mode", type=str,
                    choices=["ratio", "split_dir"])
@@ -360,6 +364,9 @@ _LEGACY_TO_STRUCTURED: dict[str, str] = {
     "slow_update_samples": "optimizer.slow_update_samples",
     "longitudinal_pair_policy": "optimizer.longitudinal_pair_policy",
     "use_meta_skill": "optimizer.use_meta_skill",
+    "use_skill_aware_reflection": "optimizer.use_skill_aware_reflection",
+    "skill_aware_appendix_source": "optimizer.skill_aware_appendix_source",
+    "skill_aware_consolidate_threshold": "optimizer.skill_aware_consolidate_threshold",
     "use_gate": "evaluation.use_gate",
     "sel_env_num": "evaluation.sel_env_num",
     "test_env_num": "evaluation.test_env_num",
@@ -527,6 +534,7 @@ def main() -> None:
     print(f"  minibatch_size: {cfg.get('minibatch_size')}")
     print(f"  seed:           {cfg.get('seed')}")
     print(f"  meta_skill:     {cfg.get('use_meta_skill', False)}")
+    print(f"  skill_aware_reflection: {cfg.get('use_skill_aware_reflection', False)}")
     print(f"  slow_update:    {cfg.get('use_slow_update', False)}")
     print(f"  out_root:       {cfg.get('out_root')}")
     print(f"{'='*60}\n")