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.
在线试用
这是真实的东西,不是模型演示。编辑输入、点击运行,Jev 会在一次往返中返回每一个类型化答案——免费、无需注册。现在想象同一次调用并行地跑在数千条数据上。
选一个示例,调整输入,然后点击 运行 Jev.
Jev 做出的决策
在一次调用中，Jev 针对同一份输入并行评估以下每一项：
Which skill should the agent use first for this request?
从这些选项中挑一个：
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)?
返回一个已校准的是/否概率。
Will this request likely need more than one skill to resolve?
返回一个已校准的是/否概率。
确切的请求
这就是实时演示背后真实的载荷——复制它，改一下 state，你就开始构建了：
{
"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?"
}
}
}把它接进你的代码
读取类型化的答案，用普通代码分支判断——无需解析。自动处理高置信度的情形，把不确定的路由给更大的模型或人工。这只是一次 API 调用，而且输出免费，所以把你需要的每个问题一次都问了吧。