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/config.py

@@ -44,6 +44,10 @@ DEFAULTS: Dict[str, Any] = {
     "gate_metric": "mixed",       # hard | soft | mixed (mixed best for tiny holdouts)
     "gate_mixed_weight": 0.5,
     "replay_mode": "mock",        # "mock" (sandboxed prompt) | "fresh" (worktree)
+    # ── dream + recall (opt-in; defaults reproduce the prior single-shot loop) ─
+    "dream_rollouts": 1,          # >1 => multi-rollout contrastive reflection per task
+    "dream_factor": 0,            # >0 => add N synthetic variants of each task to the dream
+    "recall_k": 0,                # >0 => recall the K most-similar past tasks into the dream
     "evolve_memory": True,        # consolidate CLAUDE.md
     "evolve_skill": True,         # consolidate the managed SKILL.md
     "llm_mine": True,             # use the backend to mine checkable tasks (real backends)