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 Amal-David · MCP Server · ★ 229
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-jev safe to install? View the security audit →
Awesome Jev Projects, demos, and tools built with Jev. Jev answers questions about data: pick an option, return a yes/no probability, or score something on a scale. It does not write text. The examples here show what people are using it for, with links to the code where available. 36 reviewed picks. Source notes and licenses. The larger discovery catalog is separate. Quick start · Projects · Demos from X · CUA & drivers · OpenRouter winners · Contributing Check permissions and data handling before running a project. A listing here is not a security audit. 20-second quick-start Install and set in your environment. This example makes one paid request: No key yet? prints the request without call
| Stars | 229 |
| Forks | 19 |
| Language | Python |
| Category | MCP Server |
| License | CC0-1.0 |
| Quality Score | 58.285779075542/100 |
| Open Issues | 7 |
| Last Updated | 2026-10-03 |
| Created | 2026-09-18 |
| Platforms | browser, claude-code, codex, mcp, python |
| Est. Tokens | ~22k |
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awesome-jev is Jev demos, projects, SDKs and skills, with source links and a curated X gallery.. It is categorized as a MCP Server with 229 GitHub stars.
awesome-jev is primarily written in Python. It covers topics such as agent-skills, awesome-ai, awesome-list.
You can find installation instructions and usage details in the awesome-jev GitHub repository at github.com/Amal-David/awesome-jev. The project has 229 stars and 19 forks, indicating an active community.
awesome-jev is released under the CC0-1.0 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-ai-agent-incidents, SWE-AF, Overture. 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: