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 Beever-AI · MCP Server · ★ 444
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is beever-atlas safe to install? View the security audit →
Beever Atlas Turn your team's Slack, Discord, Teams & Mattermost chats into a self-maintaining wiki — automatically. <img src="https://img.shields.io/badge/MCP-ListedonGlama-A855F7?style=for-the-badge&labelColor=4A4A4A" alt="MCP
| Stars | 444 |
| Forks | 47 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 59.6643683568449/100 |
| Open Issues | 58 |
| Last Updated | 2026-09-02 |
| Created | 2026-04-21 |
| Platforms | gemini, mcp, python |
| Est. Tokens | ~18k |
These tools work well together with beever-atlas for enhanced workflows:
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beever-atlas is Your First LLM-Wiki Conversation Knowledge Base. It is categorized as a MCP Server with 444 GitHub stars.
beever-atlas is primarily written in Python. It covers topics such as agent-memory, agents, discord-bot.
You can find installation instructions and usage details in the beever-atlas GitHub repository at github.com/Beever-AI/beever-atlas. The project has 444 stars and 47 forks, indicating an active community.
beever-atlas is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to beever-atlas on Agent Skills Hub include swarmvault, llm-wiki, orchestkit. 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: