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 gargpratyush · AI Tool · ★ 235
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is jev-router safe to install? View the security audit →
jev-router Automatic per-turn model routing for Claude Code and OpenAI Codex. Jev sends simple work to the fast tier and difficult work to the strong tier, while preserving each CLI's native interface, tools, sessions, permissions, and authentication. Both commands launch the real upstream CLI. Jev only chooses the model for a fresh user turn. Quick start Requires Node.js 20.12+ and at least one supported CLI: Claude Code or OpenAI Codex. npm package Local repository On Windows PowerShell: Get a key from TypeSafe. Then launch either interface from any repository: No Anthropic or OpenAI API key is required when the corresponding CLI is already logged in with a subscription. Every CLI argument is forwarded: bash jev-claude --
| Stars | 235 |
| Forks | 22 |
| Language | JavaScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 67.8137650656818/100 |
| Open Issues | 13 |
| Last Updated | 2026-09-19 |
| Created | 2026-09-16 |
| Platforms | claude-code, node |
| Est. Tokens | ~15k |
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jev-router is Route to the cheapest model in claude code for your task using jev-router. It is categorized as a AI Tool with 235 GitHub stars.
jev-router is primarily written in JavaScript.
You can find installation instructions and usage details in the jev-router GitHub repository at github.com/gargpratyush/jev-router. The project has 235 stars and 22 forks, indicating an active community.
jev-router is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to jev-router on Agent Skills Hub include pivotnacci, MedgeClaw, SciToolAgent. 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: