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 GeminiLight · MCP Server · ★ 669
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is MindOS safe to install? View the security audit →
MindOS Human Thinks Here, Agents Act There. English | 中文 MindOS is where you think, and where your AI agents act — a local-first knowledge base shared between you and every AI you use. Share your brain with every AI — every thought grows.
| Stars | 669 |
| Forks | 58 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.6336585206875/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-13 |
| Created | 2026-03-09 |
| Platforms | mcp, node |
| Est. Tokens | ~18k |
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MindOS is MindOS is a Human-AI Collaborative Mind System, where human thinks and agents act. Globally sync your mind for all agents: transparent, controllable, and evolving symbiotically.. It is categorized as a MCP Server with 669 GitHub stars.
MindOS is primarily written in TypeScript. It covers topics such as agent, context, knowledge-base.
You can find installation instructions and usage details in the MindOS GitHub repository at github.com/GeminiLight/MindOS. The project has 669 stars and 58 forks, indicating an active community.
MindOS is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to MindOS on Agent Skills Hub include OpenContext, openwebui-extensions, octocode-mcp. 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: