feat(sleep): experience replay + dream rollouts in the cycle (opt-in)

Wires two consolidation mechanisms into the shipped nightly cycle, both default
OFF so existing behavior is unchanged:
  - dream_rollouts (>1): multi-rollout contrastive reflection per task
  - recall_k (>0): associative recall of the K most-similar past tasks (from a
    capped task_archive persisted in state.json) into tonight's dream
  - dream_factor (>0): synthetic task variants

New shared engine module skillopt_sleep/dream.py (recall_similar, dream_augment,
dream_consolidate) is called by both the plugin cycle and the experiment harness,
so reported numbers exercise the exact shipped code. Built on the existing
rollouts_k/sample_id support already in consolidate.py/rollout.py.

Validated (5 nights x 10 real tasks/night, full held-out test, GPT-5.5, gated):
the gain scales with recall depth on a clean signal —
SearchQA recall_k=10 +3.1, recall_k=20 +4.5, full-history reference +5.6;
SpreadsheetBench (nano, gate-free) +3.6. Flat within noise on saturated/noisy
cells. See docs/sleep/EXPERIENCE_REPLAY.md (+ raw runs under blog_runs/v2_port/).

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

skillopt_sleep/state.py

@@ -28,6 +28,7 @@ DEFAULT_STATE: Dict[str, Any] = {
     "last_harvest": {},     # project -> iso timestamp of last harvested record
     "slow_memory": "",      # cross-night consolidated lessons (meta-skill analogue)
     "history": [],          # list of per-night summaries
+    "task_archive": [],     # capped list of past mined tasks (for associative recall)
 }
 
 
@@ -81,3 +82,15 @@ class SleepState:
 
     def record_night(self, summary: Dict[str, Any]) -> None:
         self.data.setdefault("history", []).append(summary)
+
+    # ── task archive (associative-recall memory) ──────────────────────────
+    def task_archive(self) -> list:
+        """Past mined tasks as plain dicts (newest last)."""
+        return list(self.data.get("task_archive", []))
+
+    def add_to_archive(self, task_dicts: list, cap: int = 300) -> None:
+        """Append tonight's tasks; keep only the most recent ``cap``."""
+        arc = self.data.setdefault("task_archive", [])
+        arc.extend(task_dicts)
+        if len(arc) > cap:
+            self.data["task_archive"] = arc[-cap:]