by agentopology · MCP Server · ★ 103
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npm install -g agentopology
Taken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 103 |
| Forks | 14 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 71/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-27 |
| Created | 2026-03-11 |
| Platforms | claude-code, codex, gemini, mcp, node |
| Est. Tokens | ~18k |
AgenTopology The Terraform for AI agents. Define your agent team once. Deploy to any platform. Claude Code · Claude Workflow · OpenClaw · Codex · Cursor · Gemini CLI · Copilot · Kiro Ships with a Claude Code skill — just type /agentopology and describe your team. The Problem Building one AI agent is easy. Building a team of agents that actually works together is brutal.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: