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)