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/cycle.py

@@ -15,7 +15,7 @@ from typing import List, Optional
 
 from skillopt_sleep.backend import get_backend
 from skillopt_sleep.config import SleepConfig, load_config
-from skillopt_sleep.consolidate import consolidate
+from skillopt_sleep.dream import dream_consolidate
 from skillopt_sleep.harvest_sources import harvest_for_config
 from skillopt_sleep.memory import ensure_skill_scaffold
 from skillopt_sleep.mine import mine
@@ -167,9 +167,21 @@ def run_sleep_cycle(
         staging_dir = ""
         return CycleOutcome(report, staging_dir, False, [])
 
-    # ── 3+4. replay + consolidate (gate) ─────────────────────────────────
-    result = consolidate(
+    # ── 3+4. replay + consolidate (gate), with opt-in dream + recall ──────
+    # recall pulls similar past tasks from the persisted archive; dream_rollouts
+    # / dream_factor enrich the training signal. With the defaults (recall_k=0,
+    # dream_rollouts=1, dream_factor=0) this is exactly the prior single-shot
+    # consolidate — behavior is unchanged unless the user opts in.
+    recall_k = int(cfg.get("recall_k", 0) or 0)
+    history_tasks = []
+    if recall_k > 0:
+        history_tasks = [TaskRecord.from_dict(d) for d in state.task_archive()]
+    result = dream_consolidate(
         backend, tasks, skill, memory,
+        history_tasks=history_tasks,
+        recall_k=recall_k,
+        dream_rollouts=int(cfg.get("dream_rollouts", 1) or 1),
+        dream_factor=int(cfg.get("dream_factor", 0) or 0),
         edit_budget=cfg.get("edit_budget", 4),
         gate_metric=cfg.get("gate_metric", "mixed"),
         gate_mixed_weight=cfg.get("gate_mixed_weight", 0.5),
@@ -178,6 +190,8 @@ def run_sleep_cycle(
         evolve_memory=cfg.get("evolve_memory", True),
         night=night,
     )
+    # archive tonight's real (non-dream) tasks so future nights can recall them
+    state.add_to_archive([t.to_dict() for t in tasks if t.origin != "dream"])
 
     report.n_replayed = len(tasks)
     report.baseline_score = result.baseline_score