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>

docs/sleep/EXPERIENCE_REPLAY.md

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+# SkillOpt-Sleep — experience replay & dream rollouts (opt-in)
+
+Two opt-in mechanisms that strengthen the nightly consolidation when your tasks
+have a clean correctness signal. Both default **off**, so enabling them is the
+only way they change behavior.
+
+## What they do
+
+| Config knob | Default | Effect |
+|---|---|---|
+| `dream_rollouts` | `1` | Run each task **K** times and learn from the *contrast* between the good and bad attempts (contrastive reflection) instead of a single failure. |
+| `recall_k` | `0` | **Associative recall** — each night, pull the `K` past tasks most similar to tonight's new ones (from a persisted task archive) into the dream, so related experience is revisited without replaying the whole history. |
+| `dream_factor` | `0` | Add `N` lightweight synthetic variants of each task to the training pool. |
+
+The validation gate still governs what ships, so these only ever *enlarge the
+signal the optimizer reflects on* — the held-out gate decides what is kept.
+
+## How to enable
+
+```jsonc
+// ~/.skillopt-sleep/config.json (or pass via the plugin's config)
+{
+  "dream_rollouts": 5,   // contrastive dreaming
+  "recall_k": 20,        // recall ~20 similar past tasks each night
+  "gate_mode": "on"      // keep the gate on (recommended)
+}
+```
+
+`recall_k` draws from a capped `task_archive` that the cycle persists in
+`state.json`, so recall becomes useful from the second night onward (once there
+is history to recall from).
+
+## Measured effect
+
+Deployment protocol (5 nights × 10 new real tasks/night, full held-out test
+sets, GPT-5.5 optimizer), run through the **same engine the plugin executes**
+(`skillopt_sleep.dream.dream_consolidate`):
+
+**SearchQA (GPT-5.5, full 1,400-item test, gated) — the gain scales with recall depth:**
+
+| Config | Δ vs baseline |
+|---|---|
+| `recall_k=10, dream_rollouts=5` | +3.1 |
+| `dream_rollouts=8` | +3.7 |
+| **`recall_k=20, dream_rollouts=5`** | **+4.5** |
+| full-history replay (reference) | +5.6 |
+
+**Second-benchmark confirmation** (SpreadsheetBench, GPT-5.4-nano, gate-free,
+shipped path): 0.279 → **0.314 (+3.6)**.
+
+## When it helps — and when it doesn't
+
+- **Helps** when tasks recur and have a checkable correctness signal (the
+  optimizer has something real to learn and the gate can verify it).
+- **Roughly flat** on saturated or noisy tasks (e.g. a strong model already near
+  ceiling) — within run-to-run noise (±1–2 points, single seed).
+- The validation gate keeps the downside bounded; keep it on by default.
+
+Trade-off: `dream_rollouts > 1` multiplies the per-night rollout cost (K×), and
+`recall_k > 0` adds the recalled tasks to each night's replay. Since the cycle
+runs offline on idle quota this is usually acceptable, but budget accordingly
+(`budget_tokens` / `budget_seconds`).
+
+Raw per-run results for the table above: `docs/sleep/blog_runs/v2_port/`.