SkillOpt is a tool for improving reusable natural-language skills used by fixed-weight language-model agents through training runs, trajectory-based edits, validation checks, and generated best_skill.md files. It is intended for developing, evaluating, and deploying agent skills without changing the underlying model weights. Catalogue add-ons support SkillOpt workflows and integrations with coding agents.
Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add microsoft/SkillOpt/plugin install skillopt-sleepWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/microsoft/skillopt/skillopt-sleep)<a href="https://agentmods.dev/skills/microsoft/skillopt/skillopt-sleep"><img src="https://agentmods.dev/badge/skills/microsoft/skillopt/skillopt-sleep/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/microsoft/skillopt/skillopt-sleep"><img src="https://agentmods.dev/badge/skills/microsoft/skillopt/skillopt-sleep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00127 | $0.02209 |
| Opus 5.5 | $0.00051 | $0.00884 |
| Sonnet 5.5 | $0.00025 | $0.00442 |
| Haiku 4.5 | $0.00013 | $0.00221 |
Grade A, and why
skillopt-sleep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.mdor 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 through the selected backend under the current skill+memory → (hard, soft) scores.
- Consolidate — reflect on failures → propose bounded edits → gate on a held-out slice; with the default gate enabled, accept only if it strictly improves.
- Stage — write the accepted
proposed_CLAUDE.mdand/orproposed_SKILL.md, plusreport.md,report.json,manifest.json, anddiagnostics.jsoninto<project>/.skillopt-sleep/staging/<timestamp>/. Nothing live changes. A rejected run still has a report but no proposed live-file replacement. - Adopt — explicit (or opt-in auto): copy staged files over live ones, backing up first.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 157 lines · 127 tokens per session scan A f8e6e40eb1ee
skillopt-sleep is a skill published in the GitHub repository microsoft/SkillOpt (18,517 stars, last pushed today), licensed MIT. It adds 127 tokens to every session and 2,209 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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