AutoSkill — Agent Tool by ECNU-ICALK

by ECNU-ICALK · Agent Tool · ★ 394

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About AutoSkill

AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution English | 中文 AutoSkill is a practical implementation of Experience-driven Lifelong Learning (ELL). It learns from real interaction experience (dialogue + agents), automatically creates reusable Skills, and continuously evolves existing Skills through merge + version updates. News 2026-05-09: Added the installable AutoSkill Local Skill Manager () for maintaining local Agent Skill files after sessions, including reusable-experience triage, similar-skill search, and / / / decisions. 2026-03-23: SkillEvo 1.0 released (Enabling models to

agent-skillscontinual-learningexperience-driven-lifelong-learningself-evolving

Quick Facts

Stars394
Forks45
LanguagePython
CategoryAgent Tool
Quality Score57.5846202483452/100
Open Issues2
Last Updated2026-05-10
Created2026-02-04
Platformspython
Est. Tokens~18629k

AutoSkill alternative? Top 6 similar tools

Looking for a AutoSkill alternative? If you're comparing AutoSkill with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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Frequently Asked Questions

What is AutoSkill?

AutoSkill is AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution. It is categorized as a Agent Tool with 394 GitHub stars.

What programming language is AutoSkill written in?

AutoSkill is primarily written in Python. It covers topics such as agent-skills, continual-learning, experience-driven-lifelong-learning.

How do I install or use AutoSkill?

You can find installation instructions and usage details in the AutoSkill GitHub repository at github.com/ECNU-ICALK/AutoSkill. The project has 394 stars and 45 forks, indicating an active community.

What are the best alternatives to AutoSkill?

The top alternatives to AutoSkill on Agent Skills Hub include COG-second-brain, Awesome-Self-Evolving-Agents, skillkit. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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