microsoft-SkillOpt/plugins/claude-code/README.md

# SkillOpt-Sleep (Claude Code plugin)

> Give your local Claude agent a **sleep cycle**. Every night it reviews your
> past sessions offline, replays your recurring tasks on your own API budget,
> and consolidates what it learns into **validated** memory (`CLAUDE.md`) and
> skills (`SKILL.md`). Your agent gets better the more you use it — no
> model-weight training.

SkillOpt-Sleep is the **deployment-time** companion to
[SkillOpt](https://github.com/microsoft/SkillOpt). SkillOpt trains a skill
offline on a benchmark; SkillOpt-Sleep applies the same discipline to *your own
daily usage*: bounded text edits, accepted only through a held-out validation
gate, with rejected edits kept as negative feedback.

It synthesizes three ideas:

| Idea | Contribution |
|---|---|
| **SkillOpt** | skill/memory = trainable text; bounded add/delete/replace edits; **held-out gate** keeps only changes that help. |
| **Claude Dreams** | offline consolidation over past sessions; input never mutated; output **reviewed then adopted**. |
| **Agent sleep** | periodic offline replay turns short-term episodes into long-term skill. |

## What it does (one "night")

```
harvest ~/.claude transcripts → mine recurring tasks → replay offline
   → consolidate (reflect → bounded edit → GATE) → stage proposal → (you) adopt
```

Nothing live is modified until **you** run `/sleep adopt` (the Dreams "review,
then adopt or discard" contract). Every adopt backs up the prior file first.

## Install

**Requirements:** Python ≥ 3.10, and the `claude` CLI (and/or `codex` CLI) on PATH.

```bash
# 1) get the code (the plugin ships inside the SkillOpt repo)
git clone https://github.com/microsoft/SkillOpt.git
cd SkillOpt

# 2) add the plugin to Claude Code as a local marketplace
/plugin marketplace add ./skillopt-sleep-plugin
/plugin install skillopt-sleep@skillopt-sleep

# 3) verify
/sleep status
```

The plugin's bundled runner (`scripts/sleep.sh`) auto-selects a Python ≥ 3.10
interpreter and calls the `skillopt_sleep` engine in the repo. No `pip install`
is required for the default `mock` backend or for `claude`/`codex` backends —
they shell out to the CLIs you already have.

## Quick start

```bash
# from inside any project you use with Claude Code:
/sleep dry-run     # safe preview: what it would learn, no changes staged
/sleep run         # full cycle: stages a reviewed proposal (still no live edits)
/sleep status      # see history + the latest staged proposal
/sleep adopt       # apply the staged proposal to CLAUDE.md / SKILL.md (with backup)
```

Or call the engine directly (Python ≥ 3.10):

```bash
python -m skillopt_sleep run --project "$(pwd)" --scope invoked --backend mock
python -m skillopt_sleep run --project "$(pwd)" --backend claude   # real lift via Claude
python -m skillopt_sleep run --project "$(pwd)" --backend codex    # real lift via Codex
```

Default backend is **`mock`** — deterministic, no API spend — so you can try the
plumbing for free. Switch to `--backend claude` or `--backend codex` for genuine
improvement on your own budget.

## Does it actually improve? (real models, public benchmark)

SkillOpt-Sleep is validated against [gbrain-evals](https://github.com/garrytan/gbrain-evals)'
public `skillopt-v1` suite — the same benchmark gbrain scores its own skill
optimizer against. We take a deliberately **deficient** skill and run one sleep
night; held-out scoring is done by a local rule judge (no judge-API, no way to
grade its own homework).

| Backend | Seed | Held-out before → after | Nights |
|---|---|---|---|
| **Claude (Haiku 4.5)** | brief-writer | **0.00 → 1.00** | 1 |
| **Codex** | brief-writer | **0.00 → 1.00** | 2 |

Both took a brief-writer with no risks section / no confidence level and, within
1–2 nights, proposed gated edits that lifted the held-out score to perfect —
into the protected `LEARNED` block, nothing else touched. The Codex 2-night
trace even shows the optimizer **diagnosing its own residual failure** and
adding a meta-rule to fix it. Full writeup + reproduction:
[`docs/sleep/real_api_results.md`](../docs/sleep/real_api_results.md).

Reproduce:

```bash
git clone https://github.com/garrytan/gbrain-evals /tmp/gbrain-evals
python -m skillopt_sleep.experiments.run_gbrain --backend claude --model haiku \
  --seeds brief-writer --data-root /tmp/gbrain-evals/eval/data/skillopt-v1 \
  --nights 1 --limit-replay 3 --limit-holdout 3
python -m skillopt_sleep.experiments.run_gbrain --backend codex \
  --seeds brief-writer --data-root /tmp/gbrain-evals/eval/data/skillopt-v1 \
  --nights 1 --limit-replay 3 --limit-holdout 3
```

## Deterministic proof (no API, no keys)

```bash
python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves
python -m skillopt_sleep.experiments.run_experiment --persona programmer  --assert-improves
```

Each prints the held-out score rising from baseline toward 1.0 as the gate
accepts the general rules your tasks need, and confirms the gate **rejects** an
injected harmful edit. Recorded output: [`docs/sleep/experiment_results.md`](../docs/sleep/experiment_results.md).

## Schedule it nightly

```bash
"${CLAUDE_PLUGIN_ROOT}/scripts/install-cron.sh" "$(pwd)"   # prints a crontab line; installs nothing
```

## Safety

- **Read-only** harvest of `~/.claude`. `mock` replay has no side effects.
- Proposals are **staged**, never auto-applied (unless you opt in with `--auto-adopt`).
- Every adopt writes a backup under the staging dir's `backup/`.
- Per-night **token/task budget caps**; secrets redacted from prompts.
- `fresh` replay (Phase 3) runs only in throwaway git worktrees.

## Status

Phase 1 (engine + deterministic experiment + plugin surface) is complete.
Phase 3 adds the real-API miner/judge and `fresh` worktree replay. See
[`docs/superpowers/specs/2026-06-07-skillopt-sleep-claude-code-plugin-design.md`](../docs/superpowers/specs/2026-06-07-skillopt-sleep-claude-code-plugin-design.md).