SkillOpt-Sleep: usage-driven self-evolution for a local Claude agent

SkillOpt-Sleep gives the user's agent a sleep cycle. On demand or on a nightly schedule, it reviews real past Claude Code sessions, re-runs recurring tasks through the selected backend, and consolidates what it learns into memory (CLAUDE.md) and skills (SKILL.md). With the default validation gate enabled, it keeps only changes that improve a held-out score. Live files change only through explicit adoption or a user-requested --auto-adopt. It aims to improve this user's recurring work, while making each accepted proposal measurable on the run's held-out tasks, 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 are recorded in the run report for review.
• Claude Dreams — consolidation that reads past sessions and proposes changes inside protected learned blocks; the input is never mutated, and output is reviewed before adoption.
• Agent sleep — periodic background 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 through the selected backend under the current skill+memory → (hard, soft) scores.
4. Consolidate — reflect on failures → propose bounded edits → gate on a held-out slice; with the default gate enabled, accept only if it strictly improves.
5. Stage — write the accepted proposed_CLAUDE.md and/or proposed_SKILL.md, plus report.md, report.json, manifest.json, and diagnostics.json into <project>/.skillopt-sleep/staging/<timestamp>/. Nothing live changes. A rejected run still has a report but no proposed live-file replacement.
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 "$(pwd)"    # no-staging preview
"${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 model-driven optimization. A held-out gain is run-specific evidence, not a guarantee of broader improvement; results depend on the tasks, model, and checks.
• Scope defaults to the invoked project; --scope all harvests every Claude project into the current run's configured targets.
• A real backend sends truncated transcript/task content to its provider. See the data-boundary rules below before using one with sensitive sessions.

Scheduling

"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" schedule --project "$(pwd)" --hour 3 --minute 17
"${CLAUDE_PLUGIN_ROOT}/scripts/sleep.sh" unschedule --project "$(pwd)"

Installs a nightly cron entry. unschedule --all removes every managed entry.

Common CLI flags

The CLI also has source/runtime path overrides (--claude-home, --codex-home, and --codex-path) and action-specific flags. Use python -m skillopt_sleep <action> --help as the authoritative surface.

Config keys (~/.skillopt-sleep/config.json)

Beyond the CLI flags, advanced behavior is controlled via config:

• preferences — free-text house rules injected into the optimizer's reflect step (e.g. "Always use async/await", "Answers in \boxed{}").
• gate_mode — on (default, validation-gated) or off (greedy, accept all edits).
• gate_metric — hard, soft, or mixed (default). Controls how the held-out gate scores.
• dream_rollouts — >1 enables multi-rollout contrastive reflection per task.
• recall_k — >0 recalls K similar past tasks into the dream (long-term memory).
• evolve_memory / evolve_skill — independently toggle CLAUDE.md vs SKILL.md consolidation.

Memory consolidation

The sleep cycle can consolidate both:

• SKILL.md — the managed skill file (bounded edits: add/delete/replace)
• CLAUDE.md — the project memory (same bounded edits)

With the default gate enabled, both are evaluated by the same held-out score. Set evolve_memory: false to consolidate only skills, or evolve_skill: false for only memory.

Hard rules

• Never hand-edit the user's CLAUDE.md / SKILL.md as part of this skill. Let the engine's explicit adopt or user-requested --auto-adopt path apply the staging manifest and back up existing live files first.
• Harvest is read-only. mock replay has no side effects.
• Real backends send truncated transcript excerpts and derived tasks to the selected provider for mining, replay, judging, and reflection. The Claude transcript path is not guaranteed to remove every secret before those calls. Review provider policy and session contents first. For sensitive data, use mock or run harvest --output <file>, inspect/redact the JSON, set "reviewed": true, and replay it with --tasks-file; real backends refuse an unreviewed task file.
• Always show the user the held-out baseline → candidate score and the exact proposed edits before suggesting adoption. Evidence before adoption.
• If asked to demonstrate the mechanism without provider calls, run python -m skillopt_sleep.experiments.run_experiment --persona researcher --json — a deterministic synthetic demo of held-out lift and gate rejection. It validates the mechanism, not effectiveness on the user's own tasks.

Validate / demo

# deterministic synthetic demo (no API): score rises and the gate blocks a regression
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 documentation for recorded results, limitations, and the supported integration surface.