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 →
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jev-gateway A local LLM gateway for coding agents. When your agent is about to decide which tool to call, the gateway asks Jev, TypeSafe's fast decision model, instead of leaving that choice to the expensive reasoning model. Everything else goes to your usual LLM untouched. It works with Codex and Claude Code out of the box, including on ChatGPT and claude.ai subscriptions, and with any client that speaks the OpenAI or Anthropic APIs. Independent project, not affiliated with or endorsed by TypeSafe. "Jev" is TypeSafe's model and this gateway is a client of its public API. Quick start You need Node.js 22.15 or newer, a TypeSafe API key, and Codex and/or Claude Code already installed and logged in. 1. Install 2. Save your TypeSafe key 3. Run your agent through the gateway 4. Watch it work That's it. Your existing login keeps working, nothing in or is changed, and plain and still behave as before. Only sessions started with the commands go through the gateway. What to expect The first or starts a small g
| Stars | 62 |
| Forks | 3 |
| Language | TypeScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 62.0054929586175/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-18 |
| Platforms | node |
| Est. Tokens | ~17k |
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jev-gateway is an open-source ai tool by vinilana with 62 GitHub stars.
jev-gateway is primarily written in TypeScript.
You can find installation instructions and usage details in the jev-gateway GitHub repository at github.com/vinilana/jev-gateway. The project has 62 stars and 3 forks, indicating an active community.
jev-gateway is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to jev-gateway on Agent Skills Hub include redesigned-pancake, claude-code-voice-skill, maui-skills. 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: