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 heyjunpenn · AI Tool · ★ 918
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-jev safe to install? View the security audit →
English · 简体中文 🌐 Explore the website · ➕ Submit a project About Awesome Jev Awesome Jev is an independent, community-maintained catalog of 433 open-source projects built with Jev, TypeSafe AI's System One model for typed decisions inside software. It is not affiliated with or endorsed by TypeSafe AI. What makes this catalog useful? Every entry identifies the concrete decision Jev makes and links to the strongest public evidence available—so you can evaluate real implementations, not ju
| Stars | 918 |
| Forks | 71 |
| Language | Astro |
| Category | AI Tool |
| License | MIT |
| Quality Score | 56.2640250292015/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-30 |
| Created | 2026-09-19 |
| Est. Tokens | ~28k |
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awesome-jev is A verified, community-maintained catalog of 962 open-source projects built with Jev.. It is categorized as a AI Tool with 918 GitHub stars.
awesome-jev is primarily written in Astro. It covers topics such as astro, awesome, awesome-jev.
You can find installation instructions and usage details in the awesome-jev GitHub repository at github.com/heyjunpenn/awesome-jev. The project has 918 stars and 71 forks, indicating an active community.
awesome-jev is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to awesome-jev on Agent Skills Hub include awesome-typesafe-jev, awesome-jev-gallery, 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: