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 Everfern-AI · Agent Tool · ★ 63
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Everfern safe to install? View the security audit →
EverFern The open-source version of Claude Cowork. Free forever, runs on your machine, no subscription required. Website • Quick Start • Features • Discord • MIT License EverFern opening Spotify and playing a song — no scripts, no automation code, just plain English. What is EverFern? EverFern is a desktop AI agent that uses your computer the way you would — clicks buttons, navigates apps, fills forms, runs workflows. You describe what you want in plain English. It figures out the steps and does it. No subscription. No cloud. Nothing leaves your machine. It's the free, open-source alternative to Claude Cowork, Manus Desktop
| Stars | 63 |
| Forks | 8 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.4884027552641/100 |
| Last Updated | 2026-08-15 |
| Created | 2026-04-11 |
| Platforms | claude-code, node |
| Est. Tokens | ~16k |
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Everfern is A free, open-source AI agent that runs entirely on your machine — controls your computer like you would, works across any app, and handles arbitrary tasks: writing code, analyzing images, organizing f. It is categorized as a Agent Tool with 63 GitHub stars.
Everfern is primarily written in TypeScript. It covers topics such as agentic-ai, ai, ai-agents.
You can find installation instructions and usage details in the Everfern GitHub repository at github.com/Everfern-AI/Everfern. The project has 63 stars and 8 forks, indicating an active community.
Everfern is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Everfern on Agent Skills Hub include frago, claude-emporium, moai-cowork. 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: