Individual skill
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 background self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay through a selected backend -> consolidate validated CLAUDE.md/SKILL.md behind a held-out gate.
plugins/claude-code/skills/skillopt-sleep/SKILL.md
Source commit 01e41dbcdf9ea6e20d6428c152c14178bd1c80f3
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 5, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
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Repository context
- Repository stars (not skill ratings)
- 18,933
- Repository last updated
- Nov 5, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- self-evolving-agents Repository topic
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Read excerpt
# 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: