by jarrodwatts · AI Tool · ★ 3.1k
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 | 3,126 |
| Forks | 601 |
| Language | TypeScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 56/100 |
| Open Issues | 3 |
| Last Updated | 2026-10-13 |
| Created | 2026-09-16 |
| Platforms | node |
| Est. Tokens | ~6k |
jev-trader One decision every Monad block. A TypeSafe Jev model watches the Kuru MON-USDC order book and answers buy or sell every 300 ms. Every block posts a real post-only limit order on that side, one tick inside the touch, replacing the last one. Fills happen when a taker hits it, so the bot earns the spread instead of paying it. A small server streams every block to the dashboard.
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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: