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 NeuZhou · MCP Server · ★ 237
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-ai-anatomy safe to install? View the security audit →
NEW: AgentSentinel -- We went from studying agent anatomy to protecting it. A zero-trust local proxy that monitors, secures, and records every decision your AI agent makes. Built from the security findings of our 16+ agent teardowns. English | 简体中文 <h
| Stars | 237 |
| Forks | 19 |
| Language | D2 |
| Category | MCP Server |
| License | MIT |
| Quality Score | 53.5202934592657/100 |
| Open Issues | 13 |
| Last Updated | 2026-04-13 |
| Created | 2026-04-04 |
| Platforms | claude-code, cli, codex, mcp |
| Est. Tokens | ~1546k |
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awesome-ai-anatomy is Source code teardowns of 14 AI coding agents. What is actually inside Claude Code, Dify, OpenHands, Cline, MemPalace, Goose, Codex CLI, and 7 more. Architecture diagrams, security analysis, design pat. It is categorized as a MCP Server with 237 GitHub stars.
awesome-ai-anatomy is primarily written in D2. It covers topics such as ai-agent, ai-coding-assistant, ai-security.
You can find installation instructions and usage details in the awesome-ai-anatomy GitHub repository at github.com/NeuZhou/awesome-ai-anatomy. The project has 237 stars and 19 forks, indicating an active community.
awesome-ai-anatomy is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-ai-anatomy on Agent Skills Hub include awesome-code-docs, agentic-ai-systems, omega-memory. 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: