Claude Skill

sentient-agi/EvoSkill

EvoSkill is an open-source Python framework that automatically discovers and synthesizes reusable agent skills from failed trajectories to boost coding agent performance.

Overview

Stars993
Forks108
LanguagePython
Last pushed2026-06-26
Last synced2026-07-03
View on GitHub

Repository

Ownersentient-agi
RepositoryEvoSkill
Full namesentient-agi/EvoSkill
Repo ID1,172,896,941

Install this Skill

pip install -e .

Registry

Typecodex_skill
Quality score85/100
Verificationreadme_parsed
Last verified2026-06-05
Platforms
ClaudeCodex
Capabilities
memorysearchimageterminal
Detected files
README.mddocsexamplespyproject.tomltests
Config keys
ANTHROPIC_API_KEYOPENAI_API_KEYOPENROUTER_API_KEYLLM_API_KEYDAYTONA_API_KEY
Install methods
  • pip install -e .
  • pip install harbor # Harbor (containerized benchmarks)
  • pip install harbor # install the Harbor CLI
  • pip install daytona

Summary

EvoSkill is an open-source framework that automatically discovers and synthesizes reusable agent skills from failed trajectories to improve coding agent performance.

Chinese description

EvoSkill — 一个开源框架,能够从失败的轨迹中自动发现并合成可复用的智能体技能,从而提升编码智能体的性能。

Key features

  • Automatically discovers reusable skills from failed agent trajectories
  • Synthesizes new skills to improve coding agent performance
  • Open-source framework built in Python
  • Designed for iterative agent improvement without manual intervention

Use cases

  • Improving coding agent accuracy by learning from past failures
  • Building a library of reusable skills for software development agents
  • Automating agent skill discovery in continuous integration pipelines
  • Enhancing autonomous code generation and debugging workflows

README excerpt

<div align="center"> <img src="./assets/evoskill_logo.png" alt="alt text" width="80%"/> <br> <h1>EvoSkill: Automated Skill Discovery for Coding Agents</h1> </div> <p align="center"> <a href="https://www.alphaxiv.org/abs/2603.02766"><img src="https://img.shields.io/badge/Paper-f73c6f?style=for-the-badge" alt="Paper"></a> <a href="https://www.sentient.xyz/blog/evoskill-automated-skill-induction-from-agent-failures"><img src="https://img.shields.io/badge/Blog-f73c6f?style=for-the-badge" alt="Blog"></a> <a href="https://sentient.xyz"><img src="https://img.shields.io/badge/Built%20by-Sentient%20Labs-f73c6f?style=for-the-badge" alt="Built by Sentient Labs"></a> <a href="https://x.com/SentientAGI"> <img src="https://img.shields.io/badge/-SentientAGI-grey?logo=x&style=for-the-badge"/> <a href="https://github.com/sentient-agi/EvoSkill/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache 2.0-007ec6?style=for-the-badge" alt="License: Apache 2.0"></a> </p> <b>Turn your general AI agents into state-of-the-art specialists with a benchmark and EvoSkill, a toolkit for automatically creating and improving AI skills, compatible with Claude Code, Codex CLI, OpenCode, OpenHands, Goose, Harbor, and more.</b> <b>EvoSkill</b> significantly extends the feedback-driven idea of <b>[GEPA](https://github.com/sentient-agi/gepa-plus)</b> from single-file optimization to complete agent evolution. Instead of only revising one prompt in place like GEPA, EvoSkill proposes multiple skill and prompt mutations jointly, evaluates new variants on held-out data, and has each iteration produce an entirely new agent program. <p align="center"> <img src="./assets/examples.png" alt="EvoSkill Architecture" style="width: 75%;"> </p> Install in seconds, then run `evo

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Data from GitHub. Synced on 2026-07-03