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 joungminsung · MCP Server · ★ 103
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenDocuments safe to install? View the security audit →
OpenDocuments Self-hosted RAG platform for AI document search across GitHub, Notion, Google Drive, Confluence, S3, local files, and web sources English | 한국어 What is OpenDocuments? OpenDocuments is an open source, self-hosted RAG (Retrieval-Augmented Gene
| Stars | 103 |
| Forks | 21 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.9051217595063/100 |
| Open Issues | 13 |
| Last Updated | 2026-07-26 |
| Created | 2026-03-27 |
| Platforms | browser, mcp, node |
| Est. Tokens | ~21k |
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OpenDocuments is Self-hosted RAG platform for AI document search across GitHub, Notion, Google Drive, local files, and web sources with citations.. It is categorized as a MCP Server with 103 GitHub stars.
OpenDocuments is primarily written in TypeScript. It covers topics such as ai, ai-search, document-qa.
You can find installation instructions and usage details in the OpenDocuments GitHub repository at github.com/joungminsung/OpenDocuments. The project has 103 stars and 21 forks, indicating an active community.
OpenDocuments is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to OpenDocuments on Agent Skills Hub include gno, rust-docs-mcp-server, knowledge-rag. 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.
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