feat(sleep): SkillOpt-Sleep plugin update (preview) — engine robustness + scheduling

Updates the SkillOpt-Sleep plugin on top of the current main. User-facing and
engine improvements since the initial drop:

* Command renamed /sleep -> /skillopt-sleep across Claude Code + Codex shells;
  refreshed plugin READMEs and install scripts.
* Built-in scheduling (skillopt_sleep/scheduler.py + __main__): schedule /
  unschedule the nightly cycle without external cron wiring.
* Backend robustness: bounded retry with backoff (no more silent empty-string
  on transient 429/timeout), content-filter-safe rollout prompt, an
  output-contract guardrail that rejects edits violating the task's required
  format, and a per-sample cache key so repeated dream rollouts are independent
  samples (fixes degenerate single-sample reflection).
* consolidate / rollout / replay: parallel multi-rollout dreaming, gate-mode
  controls, TaskRecord.system framing field.

Scope: this commit ships only the plugin engine + shells. Research/benchmark
harnesses and their data are intentionally not included; the public package
has no dependency on them (the one research-evaluator import is now guarded).
Marked as an early preview in the README; we'll keep iterating.

99/99 unit tests pass.

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

skillopt_sleep/rollout.py

@@ -58,12 +58,34 @@ def multi_rollout(
     memory: str,
     *,
     k: int = 3,
+    workers: int = 0,
 ) -> RolloutSet:
     """Run ``task`` K times. replay_one is deterministic for mock; for real
-    backends the model's own sampling yields variation across attempts."""
+    backends the model's own sampling yields variation across attempts.
+
+    The K attempts are independent, so they run concurrently (this is the dream
+    phase's dominant cost). ``workers`` defaults to the SKILLOPT_SLEEP_WORKERS
+    env (capped at k); set to 1 to force serial (used by the mock tests).
+    """
+    import os
     rs = RolloutSet(task=task)
-    for _ in range(max(1, k)):
-        rs.attempts.append(replay_one(backend, task, skill, memory))
+    k = max(1, k)
+    if workers <= 0:
+        try:
+            workers = int(os.environ.get("SKILLOPT_SLEEP_WORKERS", "1"))
+        except ValueError:
+            workers = 1
+    workers = max(1, min(workers, k))
+    if workers == 1:
+        for i in range(k):
+            rs.attempts.append(replay_one(backend, task, skill, memory, sample_id=i))
+        return rs
+    from concurrent.futures import ThreadPoolExecutor
+    with ThreadPoolExecutor(max_workers=workers) as ex:
+        futs = [ex.submit(replay_one, backend, task, skill, memory, sample_id=i)
+                for i in range(k)]
+        for f in futs:
+            rs.attempts.append(f.result())
     return rs
 
 
@@ -97,6 +119,11 @@ def contrastive_reflect(
             f"- BAD  attempt (score {rs.worst.hard:.1f}): {rs.worst.response[:200]}\n"
             f"  (bad failed: {rs.worst.fail_reason[:100]})"
         )
+    # the output contract the proposed rules must not violate (same guardrail the
+    # single-shot reflect uses — prevents harness-violating rules like "return VBA"
+    # or "ask the user for the range" on SpreadsheetBench).
+    from skillopt_sleep.backend import _task_guardrail
+    guard = _task_guardrail([(rs.task, rs.best) for rs in informative])
     prompt = (
         "You are SkillOpt's optimizer doing CONTRASTIVE reflection. For each task "
         "below the agent was run multiple times; some attempts succeeded and some "
@@ -104,6 +131,10 @@ def contrastive_reflect(
         f"and propose at most {edit_budget} SHORT, GENERAL, reusable rules for the "
         f"{target} that would make the good behavior reliable every time. Quote "
         "concrete thresholds/formats verbatim; do not paraphrase vaguely. "
+        "Every rule MUST obey the task output contract (if shown) — never propose "
+        "a rule that changes the required output format/language or tells the agent "
+        "to ask the user a question; such a rule scores ZERO.\n"
+        f"{guard}"
         'Return ONLY a JSON array: '
         '[{"op":"add","content":"<rule>","rationale":"<what good did that bad didnt>"}].\n\n'
         + "\n\n".join(blocks)