docs: move SkillOpt-Sleep into the guide; clean docs/sleep; fix guide link
Per maintainer request: - Remove the internal/scratch docs/sleep/ tree (reports, raw logs, blog run JSON, sweep.jsonl) — 23 files — and the root PUBLISHING.md. These were working notes, not reference docs. - Take the dedicated SkillOpt-Sleep content out of the main README (News bullet + section) and host it in the rendered guide instead: new section 9 in docs/guideline.html (deployment companion, the three plugins, opt-in experience replay / dream rollouts) with a sidebar entry. - Fix the README's opening reference so "Documentation & Reproduction Guide" links directly to the rendered GitHub Pages page, not the raw .html source. - Repoint the now-removed docs/sleep links in the plugin READMEs to the guide section. The plugin code (plugins/, skillopt_sleep/) is unchanged; only docs move. Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
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Yifan Yang
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[](https://microsoft.github.io/SkillOpt/) [](https://arxiv.org/abs/2605.23904) [](https://youtu.be/JUBMDTCiM0M) [](https://pypi.org/project/skillopt/) [](https://www.python.org/) [](LICENSE)
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> 📖 **For installation, data preparation, training/eval commands, the full configuration reference, and framework internals, see the [Documentation & Reproduction Guide](docs/guideline.html)** — view it [rendered online](https://htmlpreview.github.io/?https://github.com/microsoft/SkillOpt/blob/main/docs/guideline.html) or via [GitHub Pages](https://microsoft.github.io/SkillOpt/docs/guideline.html).
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> 📖 **For installation, data preparation, training/eval commands, the full configuration reference, and framework internals, see the [Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html)** (rendered on GitHub Pages).
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---
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## News 🔥🔥🔥
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- **[2026-06-14]** 😴 **SkillOpt-Sleep (preview).** A nightly *sleep cycle* for local coding agents (Claude Code / Codex / Copilot): review past sessions offline, replay recurring tasks, and consolidate validated skills behind a held-out gate. This is an early **preview** — open-source and decoupled from the paper code — that we'll keep iterating on. See [`plugins/`](plugins/) and the [section below](#-skillopt-sleep--the-deployment-time-companion).
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- **[2026-06-03]** 🎉 **[gbrain](https://github.com/garrytan/gbrain), [gbrain-evals](https://github.com/garrytan/gbrain-evals/blob/main/docs/benchmarks/2026-06-03-skillopt.md), and [darwin-skill](https://github.com/alchaincyf/darwin-skill) have all integrated SkillOpt.**
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- **[2026-06-02]** 🎉 **SkillOpt [v0.1.0](https://github.com/microsoft/SkillOpt/releases/tag/v0.1.0) is now available on [PyPI](https://pypi.org/project/skillopt/)!** Install with `pip install skillopt`. This initial release includes the full training loop (rollout → reflect → aggregate → select → update → evaluate), multi-backend support (OpenAI / Azure / Claude / Qwen / MiniMax), six built-in benchmarks, and WebUI dashboard.
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@@ -53,59 +52,6 @@ https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7
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---
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## 😴 SkillOpt-Sleep — the deployment-time companion
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> **Preview.** SkillOpt-Sleep is an early preview that we are actively iterating
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> on; interfaces and defaults may change. Feedback and issues are welcome.
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SkillOpt (above) trains a skill offline on a benchmark. **SkillOpt-Sleep**
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applies the same discipline to *your own daily usage*: it gives a local coding
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agent a nightly **sleep cycle** that reviews your past sessions, replays your
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recurring tasks on your own API budget, and consolidates what it learns into
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**validated** long-term memory and skills — behind a held-out gate, staged for
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your review. The agent gets better the more you use it, with no weight training.
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It synthesizes **SkillOpt** (validation-gated bounded text edits), **Claude
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Dreams** (offline consolidation; review-then-adopt), and the **agent sleep**
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idea (short-term experience → long-term competence). One "night":
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```
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harvest Claude Code / Codex Desktop transcripts → mine recurring tasks → replay offline
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→ consolidate (reflect → bounded edit → GATE on real held-out tasks)
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→ stage proposal → (you) adopt
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```
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**Plugins for three agents** (one engine, three thin shells — see [`plugins/`](plugins/)):
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| Platform | Folder | Install |
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|---|---|---|
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| **Claude Code** | [`plugins/claude-code`](plugins/claude-code) | `/plugin marketplace add ./plugins/claude-code` → `/skillopt-sleep` |
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| **Codex** | [`plugins/codex`](plugins/codex) | `bash plugins/codex/install.sh` → `skillopt-sleep` skill |
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| **Copilot** | [`plugins/copilot`](plugins/copilot) | register `plugins/copilot/mcp_server.py` as an MCP server |
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**Validated on real models.** On the public
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[gbrain-evals](https://github.com/garrytan/gbrain-evals) `skillopt-v1` benchmark,
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deficient skills go **0.00 → 1.00** on held-out sets with **both Claude and
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Codex** (all 4 seeds, including a real tool-use loop), cross-model transfer is
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positive, and the gate blocks regressions
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([full results](docs/sleep/FINAL_REPORT.md)).
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> **Open-source tool, decoupled from the research.** The engine lives in the
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> top-level [`skillopt_sleep/`](skillopt_sleep) package with **zero dependency**
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> on the paper's `skillopt/` experiment code (the validation gate is vendored).
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> Controls — optional gate, multi-rollout contrastive reflection, token/time
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> budget, multi-objective reward, user preferences, optimizer/target split — are
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> documented in [`docs/sleep/CONTROLLABLE_DREAMING.md`](docs/sleep/CONTROLLABLE_DREAMING.md).
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Deterministic proof (no API key): `python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves`.
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For local sleep cycles, transcript source and replay backend are separate knobs:
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use `--source claude` for Claude Code transcripts, `--source codex` for Codex
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Desktop archived sessions under `~/.codex/archived_sessions`, and
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`--backend codex` only when you want the replay/optimizer to spend Codex budget.
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---
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## Extensibility & WebUI
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### Adding a new backend
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