by XMihura · Agent Tool · ★ 205
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 | 205 |
| Forks | 30 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 11 |
| Last Updated | 2026-03-27 |
| Created | 2026-03-11 |
| Platforms | node |
| Est. Tokens | ~15k |
Kanvas Plan, track, and coordinate projects between humans and AI agents on an Obsidian Canvas board. Why This Exists AI agents work best in their CLI sandboxes — that's what companies optimize their RL training for. But you still need to plan the project, break it into tasks, see what depends on what, and track progress as you and your agents work through it.
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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: