chore(sleep): English-only across the engine, plugins, and docs

Remove every non-ASCII/CJK character for a professional open-source repo:
  - harvest.py: drop hardcoded Chinese feedback phrases; add an env-based
    extensibility hook (SKILLOPT_SLEEP_NEG_FEEDBACK / _POS_FEEDBACK) so any
    locale can be added without baking one in. Verified with a German example.
  - rollout.py / consolidate.py: English comments.
  - README.md section heading + anchor, CONTROLLABLE_DREAMING.md, plugin.json,
    marketplace.json (also fixed stale path skillopt-sleep-plugin ->
    plugins/claude-code), SKILL.md: English only.
  - Remove the internal WAKE_UP_SUMMARY.md note (not user-facing, not referenced).

Verified: zero CJK chars remain anywhere; 29 tests pass.

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

skillopt_sleep/consolidate.py

@@ -126,7 +126,7 @@ def consolidate(
     if evolve_skill:
         if rollouts_k > 1:
             # multi-rollout contrastive reflection: run each train task K times
-            # and distill a rule from the good-vs-bad contrast (the "脑补" signal).
+            # and distill a rule from the good-vs-bad contrast (the imagination signal).
             from skillopt_sleep.rollout import multi_rollout, contrastive_reflect
             sets = [multi_rollout(backend, t, cand_skill, cand_memory, k=rollouts_k)
                     for t in train_tasks]

skillopt_sleep/harvest.py

@@ -23,20 +23,30 @@ from skillopt_sleep.types import SessionDigest
 
 
 # Heuristic phrases that signal the user (dis)approving of prior output.
+# English-only by default. Users whose sessions are in another language can add
+# their own phrases via the SKILLOPT_SLEEP_NEG_FEEDBACK / _POS_FEEDBACK env vars
+# (comma-separated), so the capability is extensible without hardcoding locales.
 _NEGATIVE_FEEDBACK = (
     "still broken", "still not", "still wrong", "doesn't work", "does not work",
     "not working", "that's wrong", "thats wrong", "incorrect", "wrong",
     "no,", "nope", "fix it", "didn't", "did not", "broken", "error again",
     "still failing", "still fails", "not fixed", "revert", "undo",
-    "不对", "还是不对", "还是不行", "不行", "错了", "有问题", "没修好",
 )
 _POSITIVE_FEEDBACK = (
     "thanks", "thank you", "perfect", "great", "works now", "fixed",
     "that works", "lgtm", "looks good", "nice", "awesome", "correct",
-    "完美", "可以了", "好的", "搞定", "对了", "正确", "谢谢",
 )
 
 
+def _extra_phrases(env_var: str) -> tuple:
+    raw = os.environ.get(env_var, "")
+    return tuple(p.strip().lower() for p in raw.split(",") if p.strip())
+
+
+_NEGATIVE_FEEDBACK = _NEGATIVE_FEEDBACK + _extra_phrases("SKILLOPT_SLEEP_NEG_FEEDBACK")
+_POSITIVE_FEEDBACK = _POSITIVE_FEEDBACK + _extra_phrases("SKILLOPT_SLEEP_POS_FEEDBACK")
+
+
 def _iter_jsonl(path: str) -> Iterable[Dict[str, Any]]:
     try:
         with open(path, encoding="utf-8") as f:

skillopt_sleep/rollout.py

@@ -1,6 +1,6 @@
-"""SkillOpt-Sleep — multi-rollout + contrastive reflection ("脑补推演" core).
+"""SkillOpt-Sleep — multi-rollout + contrastive reflection (the imagination core).
 
-The user's insight: let the agent re-run the SAME task many times, then look at
+The core idea: let the agent re-run the SAME task many times, then look at
 which rollouts went well vs badly and distill a rule from the *contrast*. This
 is a much stronger learning signal than a single failure, and it is the essence
 of the offline "dream/imagination" process — train-time rollouts are synthetic,