SkillOpt/docs/sleep/WAKE_UP_SUMMARY.md

Wake-up summary — SkillOpt-Sleep (built overnight, 2026-06-07)

你睡觉时我离线把第一版做完了。下面是 TL;DR、怎么跑、关键决定、以及等你醒来要回答的问题。

TL;DR — what exists now

A working Claude Code plugin + Python engine that gives your local Claude agent a nightly sleep cycle: it reviews your past sessions offline, replays recurring tasks on your own budget, and consolidates what it learns into validated memory (CLAUDE.md) and skills (SKILL.md) — keeping a change only if it improves a held-out replay score, and only after you adopt it.

It fuses the three things you gave me:

• SkillOpt → the gate. I reuse skillopt.evaluation.gate.evaluate_gate verbatim; nightly edits are bounded and validation-gated, exactly like the paper.
• Claude Dreams → the safety model. Offline consolidation over past sessions; the input is never mutated; output is staged and reviewed, then adopted or discarded.
• Agent Sleep paper → the framing. Nightly replay turns short-term episodes into long-term competence; cross-night slow_memory is the long-term store.

It is proven to work (deterministically, no API spend):

• researcher persona: held-out 0.33 → 1.00
• programmer persona: held-out 0.32 → 1.00
• the gate rejects an injected harmful edit in both runs
• 13 stdlib tests pass, including full cycle → stage → adopt-with-backup, and parsing of your real on-disk transcripts.

Where it lives

• Worktree: /home/azureuser/yifan/Code_workspace/my_repo/SkillOpt-sleep
• Branch: feat/claude-code-sleep-plugin (2 commits, NOT pushed)
• Your fork's main was fast-forwarded locally to microsoft upstream/main (it was 40 behind; now 0/0). origin (GitHub) untouched — nothing pushed.

skillopt/sleep/                     # the engine (import-light, py>=3.10)
  harvest.py mine.py replay.py backend.py consolidate.py
  memory.py staging.py cycle.py state.py config.py types.py __main__.py
  experiments/  personas.py  run_experiment.py
skillopt-sleep-plugin/              # the Claude Code plugin
  .claude-plugin/plugin.json  commands/sleep.md  skills/skillopt-sleep/SKILL.md
  hooks/  scripts/sleep.sh  scripts/install-cron.sh  README.md
tests/test_sleep_engine.py          # 13 tests, stdlib unittest
docs/superpowers/specs/2026-06-07-...-design.md   # full design + open questions
docs/sleep/experiment_results.md    # recorded proof output

Try it yourself in 60 seconds (no API spend)

cd /home/azureuser/yifan/Code_workspace/my_repo/SkillOpt-sleep

# 1) deterministic proof it improves + gate blocks regressions
python3.12 -m skillopt.sleep.experiments.run_experiment --persona researcher --assert-improves
python3.12 -m skillopt.sleep.experiments.run_experiment --persona programmer  --assert-improves

# 2) see it mine YOUR real recent sessions (read-only)
python3.12 -m skillopt.sleep harvest --project /home/azureuser/yifan/Code_workspace --scope invoked

# 3) full run on this project (mock backend, stages a proposal, touches nothing live)
python3.12 -m skillopt.sleep run --project "$(pwd)" --scope invoked --backend mock
python3.12 -m skillopt.sleep status --project "$(pwd)"

# 4) all tests
python3.12 -m unittest tests.test_sleep_engine

(The python3.12 is because the repo needs ≥3.10 and this box's default python3 is 3.8. The plugin's scripts/sleep.sh auto-picks a good interpreter.)

Key decisions I made (so you can veto them)

1. Reused the real SkillOpt gate, didn't reinvent it. consolidate.py imports skillopt.evaluation.gate. That module imports cleanly without openai; the heavy optimizer/reflect modules (which need openai) are only touched by the future real-API path, so the mock path is dependency-free.
2. Two backends. mock = deterministic, no key, used for tests + the acceptance experiment. anthropic = real lift via your claude CLI / SDK (wired but Phase-3-shallow). Default is mock so nothing spends money without you asking.
3. Review-gated adoption by default. A night stages proposed_CLAUDE.md / proposed_SKILL.md + a report.md into <project>/.skillopt-sleep/staging/<date>/ and changes nothing live until /sleep adopt (which backs up first). --auto-adopt exists for power users but is off.
4. Edits live in a protected, marked block inside SKILL.md/CLAUDE.md, so the cycle never clobbers your hand-written content.
5. Phase boundary I hit honestly: mining your real free-text transcripts yields tasks with no exact checkable reference, so on real data the mock judge can't score lift (night → reject, 0 lift — correct, not a bug). Real lift on real transcripts needs the LLM miner + judge (Phase 3) to attach checkable references. The deterministic proof runs on persona fixtures that do have exact refs. This is documented, not hidden.

What I deliberately did NOT do

• Did not push anything (you said offline only).
• Did not spend your ANTHROPIC_API_KEY — every run above is mock. (Your key IS set; if you want, I can run the --backend anthropic demo next.)
• Did not build the Codex version (you deferred it; architecture keeps the backend pluggable).
• Did not touch your live ~/.claude/CLAUDE.md or ~/.claude/skills/*.

5 questions for you (from the design doc)

1. Adopt policy: keep default review-gated, or enable auto_adopt on your machine?
2. Scope: harvest only the invoked project, or all projects in ~/.claude/projects?
3. Real-API demo: want me to spend live budget on the --backend anthropic persona demo to show genuine (non-mock) lift?
4. Skill target: evolve a new managed skillopt-sleep-learned skill (current default), or also edit your existing hand-written skills?
5. Paper: make this a SkillOpt arXiv section/figure — "deployment-time continual skill optimization = SkillOpt gate ⊕ Dream consolidation ⊕ Sleep"? I think it's a strong story: SkillOpt provides the safe update rule that Dreams/Sleep lack.

Suggested next steps (when you're back)

• Phase 3 (highest value): real AnthropicBackend miner+judge so it lifts on your actual transcripts, not just personas; + fresh worktree replay.
• Wire slow_memory cross-night consolidation (state.py already stores it).
• pip install pytest openai anthropic in this env if you want the upstream test suite + real backend to run here (3 upstream tests currently error only because pytest isn't installed — unrelated to this branch).

Everything is committed on the branch. Nothing is pushed. Sleep well 😴