Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
npm i hono-jev-routerTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 52 |
| Forks | 4 |
| Language | TypeScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 67/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-18 |
| Created | 2026-09-18 |
| Platforms | node |
| Est. Tokens | ~15k |
hono-jev-router Route HTTP requests by meaning. is an experimental router for Hono. Instead of a method and a path, you describe a request in plain words, and Jev by TypeSafe AI decides which description fits the incoming request. [!WARNING] This is an experiment. Every semantically routed request calls a model, so it adds latency and cost, and the answer is a probability, not a guarantee.
These tools work well together with hono-jev-router for enhanced workflows:
Looking for a hono-jev-router alternative? If you're comparing hono-jev-router with other ai tool tools, these 2 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Skill to talk to Claude about your projects over the phone
.NET MAUI skills for GitHub Copilot and Claude Code
Explore other popular ai tool tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: