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README now: badges + one-line pointer to docs/guideline.html, overview, demo, sleep section, extensibility pointers, WebUI launch, citation. All run-the-demo commands live in the guideline (which already covered install, credentials, training, eval, outputs, data prep, and config). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
150 lines
8.6 KiB
Markdown
150 lines
8.6 KiB
Markdown
# SkillOpt: Executive Strategy for Self-Evolving Agent Skills
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*Train agent skills like you train neural networks — with epochs, (mini-)batchsize, learning rates, and validation gates — but without touching model weights.*
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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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---
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## News 🔥🔥🔥
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- **[2026-06-08]** 😴 **SkillOpt-Sleep is here — plugins for Claude Code, Codex, and Copilot.** Give your local coding agent a nightly *sleep cycle*: it reviews your past sessions offline, replays your recurring tasks, and consolidates validated long-term memory + skills behind a held-out gate, so it gets better the more you use it. Validated on the public [gbrain-evals](https://github.com/garrytan/gbrain-evals) `skillopt-v1` benchmark with **real Claude and Codex** (deficient skills 0.00 → 1.00 on held-out, all 4 seeds). It's an **open-source tool decoupled from the paper code**. See [`plugins/`](plugins/) and the [SkillOpt-Sleep section](#-skillopt-sleep--the-deployment-time-companion) below.
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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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---
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## Overview
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Modern agent skills are usually hand-crafted, generated one-shot by a strong
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LLM, or evolved through loosely controlled self-revision — none of which
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behaves like a deep-learning optimizer for the skill itself, and none of
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which reliably improves over its starting point under feedback.
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**SkillOpt treats the skill document as the trainable state of a frozen
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agent**, and trains it with the discipline that makes weight-space
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optimization reproducible. A separate optimizer model turns scored rollouts
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into bounded add / delete / replace edits on a single skill document; a
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candidate edit is accepted only when it strictly improves a held-out
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validation score. A textual learning-rate budget, a rejected-edit buffer,
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and an epoch-wise slow / meta update make skill training stable while
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adding **zero inference-time model calls** at deployment.
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The deployed artifact is a compact `best_skill.md` (typically 300–2,000
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tokens) that runs against the unchanged target model. Across **six
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benchmarks, seven target models, and three execution harnesses** (direct
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chat, Codex CLI, Claude Code CLI), SkillOpt is best or tied-best on **all
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52 evaluated (model, benchmark, harness) cells** and on GPT-5.5 lifts the
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average no-skill accuracy by **+23.5 points in direct chat, +24.8 inside
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the Codex agentic loop, and +19.1 inside Claude Code**. Optimized skill
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artifacts transfer across model scales, between Codex and Claude Code
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harnesses, and to nearby benchmarks without further optimization.
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For the full method, ablations, and per-cell results see the [paper](https://arxiv.org/abs/2605.23904); for a visual walkthrough of the loop see the [project page](https://microsoft.github.io/SkillOpt/); for deeper API / backend / benchmark docs see [`docs/`](docs/).
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## 🎬 Demo Video
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https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7
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<p align="center">
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<a href="https://youtu.be/JUBMDTCiM0M"><b>▶ Watch the full demo on YouTube</b></a>
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</p>
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---
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## 😴 SkillOpt-Sleep — the deployment-time companion
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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 session 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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| **Claude Code** | [`plugins/claude-code`](plugins/claude-code) | `/plugin marketplace add ./plugins/claude-code` → `/sleep` |
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| **Codex** | [`plugins/codex`](plugins/codex) | `bash plugins/codex/install.sh` → `/sleep` |
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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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---
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## Extensibility & WebUI
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### Adding a new backend
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A backend = a chat / exec target (e.g. `openai_chat`, `claude_chat`,
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`qwen_chat`, `minimax_chat`, `codex_exec`, `claude_code_exec`). See
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[`docs/guide/new-backend.md`](docs/guide/new-backend.md) for the full
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contract; in short you add a `skillopt/model/<name>_backend.py` module,
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register it in `skillopt/model/common.py` + `backend_config.py`, and wire
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it through the router in `skillopt/model/__init__.py`. `qwen_backend.py`
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and `minimax_backend.py` are good templates.
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### Adding a new benchmark
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A benchmark = a `skillopt/envs/<name>/` package with a `dataloader.py`, a
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`rollout.py`, and an `initial.md` seed skill. See
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[`docs/guide/new-benchmark.md`](docs/guide/new-benchmark.md) for the full
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contract; the simplest reference is `skillopt/envs/searchqa/`.
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### WebUI
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Launch the monitoring dashboard (optional):
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```bash
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pip install -e ".[webui]"
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python -m skillopt_webui.app
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```
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| Flag | Default | Description |
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| `--port` | 7860 | Server port |
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| `--host` | `0.0.0.0` | Bind address |
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| `--share` | off | Create a public Gradio share link |
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---
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## Citation
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```bibtex
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@misc{yang2026skilloptexecutivestrategyselfevolving,
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title={SkillOpt: Executive Strategy for Self-Evolving Agent Skills},
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author={Yifan Yang and Ziyang Gong and Weiquan Huang and Qihao Yang and Ziwei Zhou and Zisu Huang and Yan Li and Xuemei Gao and Qi Dai and Bei Liu and Kai Qiu and Yuqing Yang and Dongdong Chen and Xue Yang and Chong Luo},
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year={2026},
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eprint={2605.23904},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={https://arxiv.org/abs/2605.23904}
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}
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```
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