feat(sleep): real claude + codex backends, gbrain-evals benchmark, rule judges

Upgrade from mock-only to REAL multi-backend validation:

Backends (skillopt/sleep/backend.py):
  - CliBackend base: shared attempt/judge/reflect prompts, response cache,
    token accounting. Subclasses implement only _call().
  - ClaudeCliBackend: drives `claude -p --output-format text`.
  - CodexCliBackend: drives the REAL @openai/codex `exec -o <file>` for clean
    output; resolve_codex_path() skips the hermes wrapper at ~/.local/bin/codex.
  - reflect() now aggregates the exact failing judge criteria into the prompt
    (gbrain's lesson: tell the optimizer what the scorer rewards).

Rule judges (skillopt/sleep/judges.py): gbrain-compatible local scorers
  (section_present / regex / max_chars / contains / tool_called) — held-out
  scoring with no judge-API spend. TaskRecord gains a `judge` field +
  reference_kind="rule".

gbrain-evals adapter (experiments/gbrain_bench.py, run_gbrain.py): load
  garrytan/gbrain-evals skillopt-v1 deficient skills + train/held-out task
  sets and run our consolidate() loop against the SAME suite gbrain scores.

REAL results (docs/sleep/real_api_results.md), brief-writer seed, 1 night:
  - Claude (Haiku): held-out 0.00 -> 1.00
  - Codex:          held-out 0.00 -> 0.67
  Both proposed a correct, general format rule into the protected LEARNED block.

CLI: --backend {mock,claude,codex}, --codex-path, --model; experiment +
gbrain runners gain --limit-* cost controls. 17 tests pass.

Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>

skillopt/sleep/config.py

@@ -32,8 +32,9 @@ DEFAULTS: Dict[str, Any] = {
     "max_tokens_per_night": 400_000,
     "holdout_fraction": 0.34,     # fraction of mined tasks reserved for the gate
     # ── optimizer ──────────────────────────────────────────────────────────
-    "backend": "mock",            # "mock" | "anthropic"
+    "backend": "mock",            # "mock" | "claude" | "codex"
     "model": "",                  # backend-specific; "" => backend default
+    "codex_path": "",             # "" => auto-detect the real @openai/codex binary
     "edit_budget": 4,             # textual learning rate (max edits/night)
     "gate_metric": "mixed",       # hard | soft | mixed (mixed best for tiny holdouts)
     "gate_mixed_weight": 0.5,