by Haozhe-Xing · MCP Server · ★ 615
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/Haozhe-Xing/agent_learning.gitTaken 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 | 615 |
| Forks | 97 |
| Language | HTML |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70/100 |
| Open Issues | 2 |
| Last Updated | 2026-10-24 |
| Created | 2026-03-13 |
| Platforms | mcp |
| Est. Tokens | ~19k |
🤖 Agent Learning: Learn Agent Development from Scratch The complete open-source roadmap for learning AI Agents — from LLM basics to production-ready Agent systems. Agent Learning () is a systematic, practice-oriented AI Agent learning roadmap and hands-on tutorial covering LLM fundamentals, RAG, memory, tool use, function calling, agentic workflows, LangChain, LangGraph, MCP, multi-agent...
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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: