Jev routes models and skills by compiling a full execution plan per request
Given a plain-English request, Jev detects intent and constraints, scans 32 skills in parallel, picks among GPT-6, Claude or Gemini, and outputs a match score plus the full prompt.
I just built an AI router that decides which model and skill should solve your task before you even open ChatGPT. 1 request → 32 skills → 4 AI families → one optimized execution route. You describe the outcome in plain English. Jev analyses the intent, detects the domain and constraints, scans every available skill in parallel, then decides whether ChatGPT, Claude, Gemini or Grok is the strongest model for the job. Ask it to debug a Python API: > skill → Debugging Agent > model → GPT-6 Astra > match score → 91/100 Ask it to build an AI landing page: > skills → Website Builder + UI/UX Designer + Brand System > model → Claude Sonnet 5 > route → design and development stack Ask it to research a market: > skills → Research Analyst + Deep Research + Competitor Analyst > model → best available research model > output → evidence-backed report with primary sources It doesn’t just recommend an AI. It compiles the entire job: role → mission → skill instructions → model context → workflow → quality bar → final output format. Most people choose one AI for everything and wonder why the results are inconsistent. The model was never the only problem. The task was routed to the wrong intelligence with the wrong tools and an incomplete prompt. One request in. The right skill, model and production-ready prompt out. This is what using AI looks like when you stop guessing which model to open. Should I make the Jev Skill Router public? watch the full route compile below ↓
This post quotes another post: “A 10-step blueprint for cheaper coding agents built on Jev's architecture”