by MDalamin5 · MCP Server · ★ 104
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/MDalamin5/End-to-End-Agentic-Ai-Automation-Lab.gitTaken from the project's README on GitHub.
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.
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 | 104 |
| Forks | 42 |
| Language | Jupyter Notebook |
| Category | MCP Server |
| License | MIT |
| Quality Score | 62/100 |
| Open Issues | 1 |
| Last Updated | 2026-11-03 |
| Created | 2025-05-18 |
| Platforms | aws, docker, mcp |
| Est. Tokens | ~16k |
End-to-End Agentic AI Automation Lab Welcome to the official repository for End-to-End Agentic AI Automation Lab, a comprehensive and hands-on project portfolio developed as part of the Agentic AI and GenAI v2.0 course. This repository showcases real-world projects and advanced implementations of agentic AI systems, multi-agent frameworks, RAG pipelines, and AI workflow automation.
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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: