Use Jev for LLM tool routing

curl -sS https://router.flatkey.ai/api/alpha/decisions \
  -H "Authorization: Bearer $FLATKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "typesafe/jev-1.13",
  "state": "Turn the attached churn CSV into a two-slide summary for Monday'\''s board meeting: one chart of monthly churn by plan, one slide of takeaways.",
  "questions": {
    "skill": {
      "type": "choice",
      "instructions": "Which skill should the agent load for this request?",
      "criteria": {
        "slides": "Create or edit presentation decks and slides",
        "spreadsheet": "Analyze, clean or chart tabular data and CSV or Excel files",
        "pdf_tools": "Read, fill, split or merge PDF documents",
        "web_research": "Search the web and summarize sources",
        "frontend_design": "Design or build web pages and UI components",
        "none": "No skill is needed; the agent can answer directly"
      }
    },
    "side_effects": {
      "type": "score",
      "instructions": "What is the most consequential effect of acting on this request?",
      "criteria": [
        "read_only",
        "writes_local",
        "writes_external",
        "irreversible"
      ]
    },
    "ambiguous": {
      "type": "noul",
      "instructions": "Two or more skills fit this request about equally well."
    }
  }
}'

// Node.js 18+ (built-in fetch). Run: FLATKEY_API_KEY=... node jev.mjs
const body = {
  "model": "typesafe/jev-1.13",
  "state": "Turn the attached churn CSV into a two-slide summary for Monday's board meeting: one chart of monthly churn by plan, one slide of takeaways.",
  "questions": {
    "skill": {
      "type": "choice",
      "instructions": "Which skill should the agent load for this request?",
      "criteria": {
        "slides": "Create or edit presentation decks and slides",
        "spreadsheet": "Analyze, clean or chart tabular data and CSV or Excel files",
        "pdf_tools": "Read, fill, split or merge PDF documents",
        "web_research": "Search the web and summarize sources",
        "frontend_design": "Design or build web pages and UI components",
        "none": "No skill is needed; the agent can answer directly"
      }
    },
    "side_effects": {
      "type": "score",
      "instructions": "What is the most consequential effect of acting on this request?",
      "criteria": [
        "read_only",
        "writes_local",
        "writes_external",
        "irreversible"
      ]
    },
    "ambiguous": {
      "type": "noul",
      "instructions": "Two or more skills fit this request about equally well."
    }
  }
};

const res = await fetch("https://router.flatkey.ai/api/alpha/decisions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.FLATKEY_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify(body),
});
if (!res.ok) throw new Error(`Jev request failed: ${res.status} ${await res.text()}`);
const { answers, usage } = await res.json();
console.log(JSON.stringify(answers, null, 2), usage);

# Python 3.8+ (pip install requests). Run: FLATKEY_API_KEY=... python jev.py
import json, os, requests

body = json.loads(r"""{
  "model": "typesafe/jev-1.13",
  "state": "Turn the attached churn CSV into a two-slide summary for Monday's board meeting: one chart of monthly churn by plan, one slide of takeaways.",
  "questions": {
    "skill": {
      "type": "choice",
      "instructions": "Which skill should the agent load for this request?",
      "criteria": {
        "slides": "Create or edit presentation decks and slides",
        "spreadsheet": "Analyze, clean or chart tabular data and CSV or Excel files",
        "pdf_tools": "Read, fill, split or merge PDF documents",
        "web_research": "Search the web and summarize sources",
        "frontend_design": "Design or build web pages and UI components",
        "none": "No skill is needed; the agent can answer directly"
      }
    },
    "side_effects": {
      "type": "score",
      "instructions": "What is the most consequential effect of acting on this request?",
      "criteria": [
        "read_only",
        "writes_local",
        "writes_external",
        "irreversible"
      ]
    },
    "ambiguous": {
      "type": "noul",
      "instructions": "Two or more skills fit this request about equally well."
    }
  }
}""")

res = requests.post(
    "https://router.flatkey.ai/api/alpha/decisions",
    headers={"Authorization": f"Bearer {os.environ['FLATKEY_API_KEY']}"},
    json=body,
    timeout=60,
)
res.raise_for_status()
data = res.json()
print(json.dumps(data["answers"], indent=2), data.get("usage"))

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