Jev Use

Overview

Splits an agent loop by whether a step must produce text. Steps that only produce a decision - is the build done, which of these 30 elements to click, is this shell command safe, keep or drop this message - are handed to Jev, TypeSafe's judgment model, which answers typed yes/no, pick-one and score questions about a state in one forward pass instead of generating tokens. The agent stays the planner and the writer; Jev takes the quick calls.

Everything Jev should not decide comes back under a typed escalation contract, so the handoff is explicit in both directions rather than a guess.

When to Use This Skill

• Use when the next step is a judgment over context you already have ("did X succeed?", "which option?", "how bad is this?") rather than something to write.
• Use when you are about to check the same state several times in a row, so the questions can be batched into one call.
• Use when you want a risk check on a proposed tool call before running it.
• Do not use when the step must produce new content - text, code, free-form tool arguments - or when the options cannot be enumerated. Those are structurally the model's own work.

How It Works

Step 1: Install and wire the server

npx -y jev-use install

install wires the stdio MCP server into Claude Code, Codex and pi through each harness's own CLI - whichever it finds - and npx -y jev-use doctor verifies backend resolution with one live round trip. Set a provider credential in the environment the agent runs in (TYPESAFE_API_KEY, OPENROUTER_API_KEY or AI_GATEWAY_API_KEY, auto-detected in that order), or set JEV_BACKEND=mock to run keyless with no network calls.

Step 2: Route each step before working on it

Step 3: Batch every question about one state into one call

jev_judge takes a single state string plus a questions[] array. Latency is flat in question count, and the cost of the shared state amortizes across the batch, so 13 batched questions cost far less than 13 separate calls. Never call it once per question.

Step 4: Honor the escalation contract

Each verdict carries {id, type, answer, confidence, escalate}, plus reason and hint exactly when escalate is true. An escalated verdict is handed back to you - it is a normal verdict with a hint, never an exception:

The two pre-call reasons come from a deterministic router, so a step that was never Jev's does not spend a request.

Examples

Example 1: One state, three questions, one call

// jev_judge input
{
  "state": "CI run #142: build ok, 214 tests passed, 0 failed; 1 test quarantined as flaky last week",
  "questions": [
    { "id": "passed", "type": "noul",   "question": "Did the run fully succeed?" },
    { "id": "next",   "type": "choice", "question": "Next action?",
      "options": { "merge": "everything green", "rerun": "looks flaky", "hold": "needs attention" } },
    { "id": "risk",   "type": "score",  "question": "How risky is merging now?",
      "levels": ["routine", "worth a look", "incident"] }
  ]
}

// result (shape exact, values illustrative)
{
  "verdicts": [
    { "id": "passed", "type": "noul",   "answer": 0.97, "confidence": 0.94, "escalate": false },
    { "id": "next",   "type": "choice", "answer": "merge", "confidence": 0.34, "escalate": true,
      "reason": "unsure",
      "hint": "Treat the answer as a prior, not a decision - reason it out yourself." },
    { "id": "risk",   "type": "score",  "answer": 0.8, "confidence": 0.81, "escalate": false,
      "legend": { "0": "routine", "1": "worth a look", "2": "incident" } }
  ],
  "escalated": true
}

Two verdicts are usable immediately. The third came back escalated with reason: "unsure", so that one question - and only that one - returns to you, with Jev's answer kept as a hint.

A score answer is the expected position on your own levels: 0.8 means "between routine and worth a look, closer to the latter", and legend maps the indices back to your words.

Example 2: Risk check before a tool call

// jev_gate input
{
  "state": "Cleaning up build output in the project checkout after a failed release build",
  "tool": "Bash",
  "input": { "command": "rm -rf ./dist" }
}

// result
{ "decision": "deny", "confidence": 0.88, "hint": "..." }

jev_gate returns allow, deny or escalate for one proposed action. allow is silence: it falls through to the harness's normal permission flow, so the gate can never grant anything - it can only deny or ask. If Jev is unreachable the gate steps aside rather than granting.

Best Practices

• ✅ Do: Collect every question you have about one state and send them in a single jev_judge call.
• ✅ Do: Put the relevant facts - tool output, file excerpts, task intent - into state. Jev sees nothing else about your session.
• ✅ Do: Read escalate on every verdict before acting on answer.
• ✅ Do: Describe options and levels in your own words; a label -> meaning map sharpens a choice.
• ❌ Don't: Call jev_judge once per question.
• ❌ Don't: Route trivia. If you already know the answer, just act - a call you did not need is still a call.
• ❌ Don't: Treat an unsure answer as a decision, or treat allow from jev_gate as authorization.

Limitations

• Jev judges only what is in state; it has no view of your conversation, repository or tool history, so a thin state produces a thin judgment.
• State has a size ceiling (roughly 30k tokens). Beyond it the request escalates as oversized instead of being judged.
• Confidence is not uniform across backends: the default escalation threshold is 0.75, but through the Vercel gateway no confidence field is returned and a top-minus-runner-up margin is used instead, with a default threshold of 0.4.
• The advantage is narrower than an unconfigured comparison suggests. The project publishes its losing runs in bench/RESULTS.md: against enum-constrained baselines the latency lead is about 3x, not the 14x an unconstrained comparison shows. The benchmarks in bench/examples/ are re-runnable scripts, so check the claim on your own workload.
• This skill does not replace environment-specific validation, testing, or expert review.

Security & Safety Notes

• Data egress: whatever you put in state is sent to the provider you configure. Keep secrets, credentials and customer data out of it, or set JEV_BACKEND=mock, which judges locally with no key and no network call.
• Credentials: the provider key is read from the environment (TYPESAFE_API_KEY, OPENROUTER_API_KEY, AI_GATEWAY_API_KEY). Never paste a key into a state string, a prompt, or a committed file.
• jev_gate is not a permission system. It can deny or ask; it cannot grant. Keep your harness's own approval rules in place, and expect the gate to step aside if the backend is down.
• npx -y jev-use install edits local harness configuration through each harness's own CLI. Run it on a machine you control and re-run npx -y jev-use doctor afterwards to see what resolved.

Common Pitfalls

• Problem: Verdicts come back consistently unsure. Solution: The state is usually missing the fact the question depends on. Put the concrete tool output or file excerpt into state instead of a summary of it, and give options/levels distinguishable meanings.
• Problem: The call escalates with reason: "oversized". Solution: Trim state to the evidence the questions actually need, or split one oversized state into two smaller judged states.
• Problem: The step never reaches Jev and returns writing or open_ended. Solution: That is the router working. The step was structurally yours; do it yourself rather than rephrasing it to get past the check.