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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 | 602 |
| Forks | 63 |
| Language | JavaScript |
| Category | AI Tool |
| Quality Score | 55/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-20 |
| Platforms | node |
| Est. Tokens | ~16k |
Astra and JEV Minecraft agent This project uses GPT-6 Astra or GPT-5.6 Sol to plan and JEV to select player actions in Minecraft Java 1.16.5. It uses the official vanilla server and Mineflayer. A read-only Java sensor can report the exact dragon head position. It does not change game rules or entity state. Each selected action is sent to the game through the normal player protocol.
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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: