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io.github.shitianfang/jev-use

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Typed yes/no, pick-one and score judgments from Jev in one batched call; typed escalation back.

Source code Documentation

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

NPM package jev-use
Claude Code 
claude mcp add jev-use -- npx -y jev-use@0.8.0
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "jev-use": {
            "command": "npx",
            "args": [
                "-y",
                "jev-use@0.8.0"
            ]
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. jev-use
  2. Demos — real runs, 1× speed
  3. Install
  4. Use as a library
  5. Small enough to read
  6. Development

jev-use

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The best way for Claude Code, Codex, and pi to work with Jev: hand the tasks that need no text output to Jev — faster steps, fewer tokens, tasks done sooner and better.

It makes the LLM and Jev true collaborators: when content needs to be written, the LLM takes over; when a step just needs a fast decision, Jev executes it.

Demos — real runs, 1× speed

Directions task: Jev clicks, the LLM types — 10 decisions (p50 274 ms) · 4 writes; Jev rejects a wrong route, the LLM rewrites

Context compaction — 200 messages judged in 7 calls, one LLM paragraph replaces the dropped pile; recall 3/3

Pong: ball speed = decision latency — 86 Jev decisions in 20 s vs 6 (haiku) and 3 (gemini) called the usual way; enum-constrain both and the gap is 3×

Gate every shell command — dangerous ones denied in ~230 ms with a reason, zero LLM tokens

Every demo is a rerunnable script in bench/examples/; all numbers, methodology, variance and caveats: bench/RESULTS.md · third-party measurements: docs/evidence.md.

Install

npx -y jev-use install    # wires Claude Code, Codex, and pi — whichever it finds

Set one key in the environment your agent runs in (JEV_BACKEND=mock for a keyless dry run):

ProviderEnv var
TypeSafe directTYPESAFE_API_KEY
OpenRouterOPENROUTER_API_KEY
Vercel AI GatewayAI_GATEWAY_API_KEY

npx -y jev-use doctor checks the wiring. Judged state goes to the provider you configure; JEV_BACKEND=mock stays local. Plugin form with the routing skill and the PreToolUse gate: harness/claude-code · harness/codex.

Use as a library

npm i jev-use — zero runtime dependencies on the judgment path:

import { Jev, check, pick, rate } from "jev-use";

const jev = new Jev();

const { answers } = await jev.judge(state, {
  next: pick("Next action?", { merge: "all green", rerun: "looks flaky", hold: "needs attention" }),
  risk: rate("How risky?", ["routine", "worth a look", "incident"]),
  passed: check("Did the run fully succeed?"),
});
// answers.next → { answer: "merge", confidence: 0.93, confidenceFrom: "reported", escalate: false }

Anything Jev can't or shouldn't decide comes back with escalate: true and a typed reason. Tools, verdict shape, escalation contract, CLI: docs/reference.md.

Small enough to read

FileJob
src/protocol.tsQuestions (check/pick/rate), verdicts, escalation reasons
src/dispatch.tsPre-call routing: what never reaches Jev
src/judge.tsscreen → backend → hand back what is unsure; gate
src/jev.tsThe Jev client over that engine
src/redact.tsCredentials stripped from a gated action before it is sent
src/backends/TypeSafe, OpenRouter, Vercel, mock adapters
src/server.tsThe two MCP tools
src/cli.tsinstall, serve, hook gate, doctor
skills/jev-use/SKILL.mdThe routing rules the agent follows

Development

$ npm run typecheck && npm test    # unit tests incl. per-provider wire fixtures
$ npm run smoke                    # real MCP client ↔ built CLI over stdio
$ node bench/run.mjs               # micro-benchmarks, your key and region

Substantially written with Claude Code (AI-assisted).

MIT © shitianfang

Frequently asked questions

What is io.github.shitianfang/jev-use?

Typed yes/no, pick-one and score judgments from Jev in one batched call; typed escalation back.

How do I install io.github.shitianfang/jev-use?

It runs on your machine. In Claude Code: claude mcp add jev-use -- npx -y jev-use@0.8.0. For other clients, paste the JSON configuration from this page into their MCP settings.

Does io.github.shitianfang/jev-use need an API key?

Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.

Is io.github.shitianfang/jev-use open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains io.github.shitianfang/jev-use?

It is published by shitianfang. AI.tn lists it but does not maintain or endorse it.

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