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)

1. Harvest — read ~/.claude/projects/*/<session>.jsonl + ~/.claude/history.jsonl (READ-ONLY) → session digests.
2. Mine — digests → TaskRecords (recurring intents + outcome labels + checkable refs where possible).
3. Replay — re-run tasks offline under the current skill+memory → (hard, soft) scores.
4. Consolidate — reflect on failures → propose bounded edits → gate on a held-out slice; accept only if it strictly improves.
5. Stage — write proposed_CLAUDE.md, proposed_SKILL.md, a diff, and report.md into <project>/.skillopt-sleep/staging/<date>/. Nothing live changes.
6. 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 the SkillOpt-Sleep guide section for recorded output and docs/superpowers/specs/2026-06-07-skillopt-sleep-claude-code-plugin-design.md for the full design.