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 TrySita · MCP Server · ★ 198
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AutoDocs safe to install? View the security audit →
Follow Us Sohan - @SohanSarabu Adi - @adiperswal Docs · Report Bug · Feature Request · Sita uses GitHub Discussions for Support and Feature Requests. Your browser does not support the video tag. AutoDocs, by Sita Automate docu
| Stars | 198 |
| Forks | 14 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 71.8626388183532/100 |
| Open Issues | 3 |
| Last Updated | 2026-06-06 |
| Created | 2025-09-07 |
| Platforms | docker, mcp, python |
| Est. Tokens | ~2185k |
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AutoDocs is We handle what engineers and IDEs won't: generating and maintaining technical documentation for your codebase, while also providing search with dependency-aware context to help your AI tools understan. It is categorized as a MCP Server with 198 GitHub stars.
AutoDocs is primarily written in Python. It covers topics such as agents, ai, ast.
You can find installation instructions and usage details in the AutoDocs GitHub repository at github.com/TrySita/AutoDocs. The project has 198 stars and 14 forks, indicating an active community.
AutoDocs is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to AutoDocs on Agent Skills Hub include mcpcan, oreilly-ai-agents, flow-like. 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: