No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by AppitStudio · AI Tool · ★ 58
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-jev safe to install? View the security audit →
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. Explore Jev-powered apps · Share your app Try the beta web UI at JevList. Explore the projects in this directory through a searchable web interface. We're continually improving the experience—take a look and let us know what you think! Contents Explore Jev with our skill Start here Explore practical use cases Official SDKs and tools Community projects Apps powered by Jev Browse the project directory Tools and integrations Computer and browser use Starter projects Reference project Patterns and cookbooks [Routing and classifi
| Stars | 58 |
| Forks | 6 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 63.0117236109288/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-18 |
| Platforms | python |
| Est. Tokens | ~19k |
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awesome-jev is Curated Jev resources and runnable examples for typed AI decisions.. It is categorized as a AI Tool with 58 GitHub stars.
awesome-jev is primarily written in Python. It covers topics such as ai, awesome, awesome-list.
You can find installation instructions and usage details in the awesome-jev GitHub repository at github.com/AppitStudio/awesome-jev. The project has 58 stars and 6 forks, indicating an active community.
The top alternatives to awesome-jev on Agent Skills Hub include awesome-jev-projects, awesome-jev, awesome-jev. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: