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 johnxie · MCP Server · ★ 59
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-code-docs safe to install? View the security audit →
Awesome Code Docs 203 deep-dive tutorials for AI agents, LLM frameworks & coding tools Learn how complex systems actually work — not just what they do Browse Tutorials · A-Z Directory · Query Hub · Intent Map · Market Signals · Learning Paths · Contributing · Community Why This Exists Most documentation tells you what to do. These tutorials explain how and why complex systems work under the hood — with architecture diagrams, real code walkthroughs, a
| Stars | 59 |
| Forks | 10 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.0499353629761/100 |
| Last Updated | 2026-09-14 |
| Created | 2025-09-04 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~25k |
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awesome-code-docs is 203 deep-dive tutorials for AI agents, LLM frameworks, coding tools, MCP, and open-source developer platforms. It is categorized as a MCP Server with 59 GitHub stars.
awesome-code-docs is primarily written in Python. It covers topics such as ai-agents, ai-coding-assistant, ai-tutorial.
You can find installation instructions and usage details in the awesome-code-docs GitHub repository at github.com/johnxie/awesome-code-docs. The project has 59 stars and 10 forks, indicating an active community.
awesome-code-docs is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-code-docs on Agent Skills Hub include awesome-ai-anatomy, claudepro-directory, c4-genai-suite. 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: