Microsoft just open-sourced one of the most interesting agent engineering projects I’ve seen this year…

→ https://github.com/microsoft/SkillOpt…

Most engineers assume improving an AI agent requires:

Better models More data More fine-tuning

SkillOpt takes a completely different approach.

Instead of optimizing the model…

It optimizes the skill.

Think of a skill as a standard operation procedure (SOP) for an AI agent.

A small document that describes:

How to solve a task Which tools to use Which steps to follow How to format outputs What good behavior looks like

Traditionally, these skill files are written once and then forgotten.

SkillOpt turns them into something trainable.

The idea is surprisingly simple:

1. Run the agent on a batch of tasks
2. Measure performance
3. Let a second model analyze failures
4. Propose edits to the skill document
5. Keep only the edits that improve validation performance

The loop starts looking surprisingly similar to machine learning.

… except you’re training the skill rather than the weights.

We’ve spent years treating skills, prompts, and agent instructions as static artifacts.

SkillOpt treats them as something that can improve continuously.

The model stays the same.

The skill evolves.

Learn more here: https://github.com/microsoft/SkillOpt…

microsoft/SkillOpt

Source: https://github.com/microsoft/SkillOpt

SkillOpt: Executive Strategy for Self-Evolving Agent Skills

Train agent skills like you train neural networks — with epochs, (mini-)batchsize, learning rates, and validation gates — but without touching model weights.

📖 For installation, data preparation, training/eval commands, the full configuration reference, and framework internals, see the Documentation & Reproduction Guide (rendered on GitHub Pages).

News 🔥🔥🔥

• [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 for what it is, how to use it, and results.
• [2026-06-03] 🎉 gbrain, gbrain-evals, and darwin-skill have all integrated SkillOpt.
• [2026-06-02] 🎉 SkillOpt v0.1.0 is now available on PyPI! 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.

Overview

Modern agent skills are usually hand-crafted, generated one-shot by a strong LLM, or evolved through loosely controlled self-revision — none of which behaves like a deep-learning optimizer for the skill itself, and none of which reliably improves over its starting point under feedback.

SkillOpt treats the skill document as the trainable state of a frozen agent, and trains it with the discipline that makes weight-space optimization reproducible. A separate optimizer model turns scored rollouts into bounded add / delete / replace edits on a single skill document; a candidate edit is accepted only when it strictly improves a held-out validation score. A textual learning-rate budget, a rejected-edit buffer, and an epoch-wise slow / meta update make skill training stable while adding zero inference-time model calls at deployment.

The deployed artifact is a compact best_skill.md (typically 300–2,000 tokens) that runs against the unchanged target model. Across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex CLI, Claude Code CLI), SkillOpt is best or tied-best on all 52 evaluated (model, benchmark, harness) cells and on GPT-5.5 lifts the average no-skill accuracy by +23.5 points in direct chat, +24.8 inside the Codex agentic loop, and +19.1 inside Claude Code. Optimized skill artifacts transfer across model scales, between Codex and Claude Code harnesses, and to nearby benchmarks without further optimization.

For the full method, ablations, and per-cell results see the paper; for a visual walkthrough of the loop see the project page; for deeper API / backend / benchmark docs see docs/.

🎬 Demo Video

https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7

▶ Watch the full demo on YouTube

Extensibility & WebUI

Adding a new backend

A backend = a chat / exec target (e.g. openai_chat, claude_chat, qwen_chat, minimax_chat, codex_exec, claude_code_exec). See docs/guide/new-backend.md for the full contract; in short you add a skillopt/model/<name>_backend.py module, register it in skillopt/model/common.py + backend_config.py, and wire it through the router in skillopt/model/__init__.py. qwen_backend.py and minimax_backend.py are good templates.

Adding a new benchmark

A benchmark = a skillopt/envs/<name>/ package with a dataloader.py, a rollout.py, and an initial.md seed skill. See docs/guide/new-benchmark.md for the full contract; the simplest reference is skillopt/envs/searchqa/.

WebUI

Launch the monitoring dashboard (optional):

pip install -e ".[webui]"
python -m skillopt_webui.app

Citation

@article{yang2026skillopt,
  title={Skillopt: Executive strategy for self-evolving agent skills},
  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},
  journal={arXiv preprint arXiv:2605.23904},
  year={2026}
}