by shitianfang
Claude Code / Codex / pi plugin that hands agent steps needing no text output to Jev (TypeSafe's judgment model) — measured p50 ~230 ms and ~$0.02 per 1,000 judgments, with typed escalation back to the LLM
# Add to your Claude Code skills
git clone https://github.com/shitianfang/jev-useSee how jev-use compares with popular alternatives.
jev-use is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by shitianfang. Claude Code / Codex / pi plugin that hands agent steps needing no text output to Jev (TypeSafe's judgment model) — measured p50 ~230 ms and ~$0.02 per 1,000 judgments, with typed escalation back to the LLM. It has 50 GitHub stars.
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Clone the repository with "git clone https://github.com/shitianfang/jev-use" and add it to your Claude Code skills directory (see the Installation section above).
jev-use is primarily written in JavaScript. It is open-source under shitianfang on GitHub, so you can review or fork the full source.
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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.
Every demo is a rerunnable script in bench/examples/; all numbers, methodology, variance and caveats: bench/RESULTS.md · third-party measurements: docs/evidence.md.
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):
| Provider | Env var |
|---|---|
| TypeSafe direct | TYPESAFE_API_KEY |
| OpenRouter | OPENROUTER_API_KEY |
| Vercel AI Gateway | AI_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.
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.
| File | Job |
|---|---|
| src/protocol.ts | Questions (check/pick/rate), verdicts, escalation reasons |
| src/dispatch.ts | Pre-call routing: what never reaches Jev |
| src/judge.ts | screen → backend → hand back what is unsure; gate |
| src/jev.ts | The Jev client over that engine |
| src/redact.ts | Credentials stripped from a gated action before it is sent |
| src/backends/ | TypeSafe, OpenRouter, Vercel, mock adapters |
| src/server.ts | The two MCP tools |
| src/cli.ts | install, serve, hook gate, doctor |
| skills/jev-use/SKILL.md | The routing rules the agent follows |
$ 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