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Yifan Yang 86bad36ffe 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>

2026-06-14 16:12:00 +00:00

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name, description

name	description
skillopt-sleep	Use when the user wants their Claude agent to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, memory/skill consolidation, or says things like 'make my agent better the more I use it', 'review my past sessions', 'learn my preferences', 'consolidate what you learned', 'run the sleep cycle', or wants to schedule offline self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay offline -> consolidate validated CLAUDE.md/SKILL.md behind a held-out gate.

name

description

skillopt-sleep

Use when the user wants their Claude agent to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, memory/skill consolidation, or says things like 'make my agent better the more I use it', 'review my past sessions', 'learn my preferences', 'consolidate what you learned', 'run the sleep cycle', or wants to schedule offline self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay offline -> consolidate validated CLAUDE.md/SKILL.md behind a held-out gate.

SkillOpt-Sleep: offline self-evolution for a local Claude agent

SkillOpt-Sleep gives the user's agent a sleep cycle. While the user is offline (e.g. nightly), it reviews their real past Claude Code sessions, re-runs recurring tasks on their own API budget, and consolidates what it learns into memory (CLAUDE.md) and skills (SKILL.md) — but only keeps changes that pass a held-out validation gate, and only after the user adopts them. The agent gets measurably better at this user's recurring work, with no model-weight training. It is the deployment-time analogue of training: short-term experience → long-term competence.

It synthesizes three ideas:

SkillOpt — the skill/memory doc is trainable text; bounded add/delete/replace edits; accepted only through a held-out gate; rejected edits become negative feedback.
Claude Dreams — offline consolidation that reads past sessions and rebuilds memory (dedup/merge/resolve); the input is never mutated; output is reviewed then adopted.
Agent sleep — periodic offline replay turns episodes into durable skill.

When to use this skill

Trigger when the user wants any of:

"make my agent learn from how I use it" / "get better the more I use it" / "remember my preferences across sessions"
a nightly/scheduled or on-demand offline self-improvement / dream / sleep run
to review past sessions/trajectories and distill recurring tasks
to consolidate feedback into CLAUDE.md or a managed skill
to schedule the cycle (cron) or adopt a staged proposal

The cycle (six stages)

Harvest — read ~/.claude/projects/*/<session>.jsonl + ~/.claude/history.jsonl (READ-ONLY) → session digests.
Mine — digests → TaskRecords (recurring intents + outcome labels + checkable refs where possible).
Replay — re-run tasks offline under the current skill+memory → (hard, soft) scores.
Consolidate — reflect on failures → propose bounded edits → gate on a held-out slice; accept only if it strictly improves.
Stage — write proposed_CLAUDE.md, proposed_SKILL.md, a diff, and report.md into <project>/.skillopt-sleep/staging/<date>/. Nothing live changes.
Adopt — explicit (or opt-in auto): copy staged files over live ones, backing up first.

How to drive it

Prefer the /skillopt-sleep command. Under the hood it calls the bundled runner:

"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" status                       # what's happened
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" dry-run --project "$(pwd)"    # safe preview
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" run --project "$(pwd)"        # full cycle, stages a proposal
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" adopt --project "$(pwd)"      # apply staged proposal (with backup)

Default backend is mock (deterministic, no API spend) — good for trying the plumbing.
Add --backend claude or --backend codex to spend the user's real budget for genuine improvement.
Scope defaults to the invoked project; --scope all harvests every project.

Hard rules

Never hand-edit the user's CLAUDE.md / SKILL.md as part of this skill. Only the adopt action changes live files, and it backs them up first.
Harvest is read-only. mock replay has no side effects.
Always show the user the held-out baseline → candidate score and the exact proposed edits before suggesting adoption. Evidence before adoption.
If asked whether it really helps, run python -m skillopt_sleep.experiments.run_experiment --persona researcher --json — a deterministic demo that proves held-out lift and that the gate blocks harmful edits.

Validate / demo

# deterministic proof (no API): held-out score rises, gate blocks regressions
python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves
python -m skillopt_sleep.experiments.run_experiment --persona programmer  --assert-improves

See docs/sleep/experiment_results.md for recorded output and docs/superpowers/specs/2026-06-07-skillopt-sleep-claude-code-plugin-design.md for the full design.

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