feat(sleep): multi-objective reward (accuracy/tokens/latency) + user preferences

- ReplayResult records per-rollout tokens + latency_ms; replay_one measures them
  (approximated from text length when the backend doesn't track tokens, e.g. mock).
- replay.multi_objective_reward(w_acc, w_tokens, w_latency): weighted reward so a
  skill can be optimized to be cheaper/faster, not only more accurate (cost terms
  normalized vs a reference, default = accuracy-only / backward compatible).
- Backend.preferences (free text) injected into reflect as a prior; build_backend
  attaches it (to the optimizer for dual backends). run_gbrain gains --preferences.

3 new tests (multi-objective ordering, preference injection, cost recording).
29 tests pass; mock gates + 3.8/3.12 compile green.

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

skillopt/sleep/types.py

@@ -95,6 +95,8 @@ class ReplayResult:
     task_type: str = "task"
     judge_rationale: str = ""
     tools_called: List[str] = field(default_factory=list)
+    tokens: int = 0                   # approx tokens this rollout cost (for token objective)
+    latency_ms: float = 0.0           # wall-clock for this rollout (for latency objective)
 
     def to_dict(self) -> Dict[str, Any]:
         return asdict(self)