by azalio · Claude Skill · ★ 156
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. 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 | 156 |
| Forks | 8 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 69/100 |
| Open Issues | 10 |
| Last Updated | 2026-10-22 |
| Created | 2025-10-06 |
| Platforms | claude-code, cli, codex, python |
| Est. Tokens | ~17k |
Why MAP Exists Most AI agents rush straight to code — fast wrong answers, silent rework. Common failure modes MAP eliminates: AI silently makes architecture decisions you did not approve. One prompt produces a large diff that is hard to review. Tests are written around the generated implementation, including its mistakes. The output compiles, but you cannot explain why the design is correct.
These tools work well together with map-framework 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: