by RiccardoBiosas · MCP Server · ★ 485
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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 | 485 |
| Forks | 128 |
| Language | Astro |
| Category | MCP Server |
| License | MIT |
| Quality Score | 59/100 |
| Open Issues | 27 |
| Last Updated | 2026-11-03 |
| Created | 2023-04-01 |
| Platforms | mcp |
| Est. Tokens | ~25k |
Awesome MLSecOps: Machine Learning and AI Security Resources 🛡️🤖 What is MLSecOps? MLSecOps (Machine Learning Security Operations) is the practice of integrating security throughout the machine learning lifecycle—from data collection and model development to deployment, monitoring, and incident response.
These tools work well together with awesome-MLSecOps for enhanced workflows:
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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: