Agent skill routing
Pick which skill or tool a coding agent should use for a request — or decline when none fit.
"Skill and tool selection" is the first coding-agent pattern in the community awesome-jev catalog: as an agent's toolbox grows to dozens of skills, deciding which one a turn needs becomes a real bottleneck. Keyword or embedding matching can't say "none of these" — it always returns its closest guess. A Jev choice returns one skill with per-option probabilities, so a weak match falls below your threshold and the agent declines or asks the big model, instead of confidently firing the wrong tool. That's the difference between an agent that misroutes silently and one that knows when it doesn't know.
Try it live
This is the real thing, not a mockup. Edit the input, hit Run, and Jev returns every typed answer in one round trip — free, no signup. Now picture the same call fired across thousands of items in parallel.
Pick a demo, tweak the input, and hit Run Jev.
The decisions Jev makes
In a single call, Jev evaluates each of these — in parallel, against the same input:
Which skill should the agent use first for this request?
picks one of these options:
run_tests— run the test suiteread_logs— fetch and search recent logsedit_code— modify source filessearch_web— look up external docs/errorsopen_pr— open a pull requestnone— no available skill clearly fits — ask or escalate
Is the top skill a clearly correct fit (safe to auto-run without asking)?
returns a calibrated yes/no probability.
Will this request likely need more than one skill to resolve?
returns a calibrated yes/no probability.
The exact request
This is the real payload behind the live demo — copy it, change the state, and you're building:
{
"model": "jev-latest",
"state": "An AI coding agent receives: \"The staging deploy is failing with a 502 right after the container starts — can you figure out why?\"\n\nAvailable skills:\n- run_tests: run the project's test suite\n- read_logs: fetch and search recent service/deploy logs\n- edit_code: modify source files\n- search_web: look up external docs/errors\n- open_pr: open a pull request with changes",
"questions": {
"skill": {
"type": "choice",
"instructions": "Which skill should the agent use first for this request?",
"criteria": {
"run_tests": "run the test suite",
"read_logs": "fetch and search recent logs",
"edit_code": "modify source files",
"search_web": "look up external docs/errors",
"open_pr": "open a pull request",
"none": "no available skill clearly fits — ask or escalate"
}
},
"confident": {
"type": "noul",
"instructions": "Is the top skill a clearly correct fit (safe to auto-run without asking)?"
},
"needs_followup": {
"type": "noul",
"instructions": "Will this request likely need more than one skill to resolve?"
}
}
}Wire it into your code
Read the typed answers and branch in plain code — no parsing. Auto-handle the high-confidence cases and route the uncertain ones to a bigger model or a human. It's one API call and output is free, so ask every question you need at once.
Build your own
Every scenario above is a single API call. Try any of them free in the playground, then get a hosted key to ship it in minutes.