by AppitStudio · AI Tool · ★ 112
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npx skills add AppitStudio/awesome-jev --skill awesome-jev-guideTaken from the project's README on GitHub.
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 | 112 |
| Forks | 23 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 63/100 |
| Open Issues | 3 |
| Last Updated | 2026-11-04 |
| Created | 2026-09-18 |
| Platforms | python |
| Est. Tokens | ~19k |
Awesome Jev Discover Jev-powered apps, developer resources, and runnable decision examples. Jev is TypeSafe AI's System One model: give it state and typed questions, then use the returned choices, scores, and probabilities in your code. This independent community directory collects applications that use Jev, resources for developers, and small, inspectable workflows to learn from.
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A curated list of Jev use cases, projects, SDKs, and resources. Jev is TypeSafe AI's System One model for fast
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A community directory of projects built on Jev, TypeSafe AI's System One model.
Jev / TypeSafe System One 中文精选列表：官方资料、SDK、爆款应用、Agent 工具、开源复现与独立评测，附中文上手指南，每日自动收录 GitHub 热门项目。
Typed decisions with TypeSafe's Jev, the first System One model
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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: