Yif-Yang
114 lines
7.8 KiB
Markdown
114 lines
7.8 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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<p align="center">
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<a href="https://trendshift.io/repositories/38498?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-38498" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/38498/daily?language=Python" alt="microsoft%2FSkillOpt | Trendshift" width="250" height="55"/></a>
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<a href="https://trendshift.io/repositories/38498?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-38498" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/38498/weekly?language=Python" alt="microsoft%2FSkillOpt | Trendshift" width="250" height="55"/></a>
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</p>
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> 📖 **For installation, data preparation, training/eval commands, configuration, and framework internals, start with the versioned [SkillOpt documentation](https://github.com/microsoft/SkillOpt/blob/main/docs/index.md). A concise rendered overview is available in the [Documentation & Reproduction Guide](https://microsoft.github.io/SkillOpt/docs/guideline.html), and longer-form engineering analysis appears on the [Technical Blog](https://microsoft.github.io/SkillOpt/blog/). We also maintain a [Changelog](CHANGELOG.md) for released and unreleased changes.**
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---
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## News 🔥🔥🔥
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- **[2026-07-02]** 🚀 **SkillOpt [v0.2.0](https://github.com/microsoft/SkillOpt/releases/tag/v0.2.0) is out on [PyPI](https://pypi.org/project/skillopt/)!** Headline feature: **SkillOpt-Sleep**, a nightly offline self-evolution engine (harvest → mine → replay → consolidate behind a held-out validation gate), now shipped as the `skillopt-sleep` CLI. It also includes experimental multi-objective, replay, and dream-rollout controls; the main CLI keeps conservative defaults and does not expose every experiment-harness control as a flag. The release source adds integration shells for **Claude Code, Codex, Copilot, and Devin**, plus an **OpenClaw reference adaptation**; these plugin/MCP files live in the repository rather than the PyPI wheel. It also adds SearchQA split materialization, Windows robustness, and hardened JSON parsing. See the [release notes](https://github.com/microsoft/SkillOpt/releases/tag/v0.2.0) for full release details and contributor acknowledgements.
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- **[2026-06-15]** 😴 **SkillOpt-Sleep (preview)** — a nightly offline self-evolution companion for local coding agents (Claude Code / Codex / Copilot): review past sessions, replay recurring tasks, and consolidate validated skills behind a held-out gate. See **[`docs/sleep/README.md`](docs/sleep/README.md)** for what it is, how to use it, and results.
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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; in the
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default paper-style path, a candidate edit is accepted only when it strictly
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improves a held-out 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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## 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`, `openai_compatible`, `codex_exec`,
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`claude_code_exec`). If a provider implements the OpenAI Chat Completions
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protocol, try the built-in `openai_compatible` backend before adding code. 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 an adapter, a data loader,
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a scored rollout helper, a YAML config, and optionally an initial seed skill.
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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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The default host listens on every network interface. Use
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`--host 127.0.0.1` for local-only access.
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---
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## Citation
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```bibtex
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@article{yang2026skillopt,
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title={Skillopt: Executive strategy for self-evolving agent skills},
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author={Yang, Yifan and Gong, Ziyang and Huang, Weiquan and Yang, Qihao and Zhou, Ziwei and Huang, Zisu and Li, Yan and Gao, Xuemei and Dai, Qi and Liu, Bei and others},
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journal={arXiv preprint arXiv:2605.23904},
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year={2026}
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}
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```
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