by microsoft · AI 工具 · ★ 18.5k
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. 🎬 SkillOpt Demo Video https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7 ▶ Watch the full demo on YouTube Install Requirements: Python 3.10+ Configure API Credentials Azure OpenAI (recommended): Note: is required for all three modes (, , ). Without it, all LLM calls will fail.
| Stars | 18,532 |
| Forks | 1,760 |
| Language | Python |
| Category | AI 工具 |
| License | MIT |
| Quality Score | 74.0364977272224/100 |
| Open Issues | 54 |
| Last Updated | 2026-10-18 |
| Created | 2026-05-08 |
| Platforms | python |
| Est. Tokens | ~15k |
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SkillOpt is SkillOpt is a text-space optimizer that trains reusable natural-language skills for frozen LLM agents through trajectory-driven edits, validation-gated updates, and deployable best_skill.md artifacts.. It is categorized as a AI 工具 with 18.5k GitHub stars.
SkillOpt is primarily written in Python. It covers topics such as agent-skills, self-evolving-agents.
You can find installation instructions and usage details in the SkillOpt GitHub repository at github.com/microsoft/SkillOpt. The project has 18.5k stars and 1760 forks, indicating an active community.
SkillOpt is released under the MIT license, making it free to use and modify according to the license terms.