No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by ECNU-ICALK · Agent Tool · ★ 546
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AutoSkill safe to install? View the security audit →
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
| Stars | 546 |
| Forks | 51 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 57.5846202483452/100 |
| Open Issues | 2 |
| Last Updated | 2026-05-10 |
| Created | 2026-02-04 |
| Platforms | python |
| Est. Tokens | ~18629k |
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AutoSkill is AutoSkill: Experience-Driven Lifelong Learning via Skill Self-Evolution. It is categorized as a Agent Tool with 546 GitHub stars.
AutoSkill is primarily written in Python. It covers topics such as agent-skills, continual-learning, experience-driven-lifelong-learning.
You can find installation instructions and usage details in the AutoSkill GitHub repository at github.com/ECNU-ICALK/AutoSkill. The project has 546 stars and 51 forks, indicating an active community.
The top alternatives to AutoSkill on Agent Skills Hub include SkillClaw, COG-second-brain, Awesome-Self-Evolving-Agents. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: