jev-code

Jev on OpenRouter as a classifier tool inside Claude Code, Codex, Pi, and OpenCode.
Typed labels, yes/no checks, scores, and rankings with calibrated probabilities, in a few hundred milliseconds.

Coding agents make small classification decisions all day: which of these 40 CI failures are real, which files answer this question, does this PR do what its description says, how severe is each finding. They usually do it by eyeballing, by writing a regex, or by burning a frontier-model call per item.

Jev is a decision model rather than a text model: you send evidence and typed questions, it returns typed answers with calibrated probabilities, never prose. jev-code turns that into a first-class tool for four coding agents and ships a skill that teaches the agent when to reach for it.

┌──────────────┐  jev_classify / jev_check / ...  ┌───────────┐ POST /api/v1/systemone ┌──────────────┐
│ Claude Code  │ ───── MCP (stdio) ─────────────▶ │           │ ───────────────────▶ │              │
│ Codex        │ ───── MCP (stdio) ─────────────▶ │  jev-code │                      │ OpenRouter   │
│ OpenCode     │ ───── MCP (stdio) ─────────────▶ │           │ ◀─────────────────── │   (Jev)      │
│ Pi           │ ───── native extension ────────▶ │           │  typed answers +     │              │
│ any shell    │ ───── jev-code CLI ────────────▶ │           │  probabilities       │              │
└──────────────┘                                  └───────────┘                      └──────────────┘

What you get

Quick start

1. Get a key at OpenRouter and export it:

export OPENROUTER_API_KEY=sk-or-...

2. Install into your agents (Node.js 20+):

npx -y @goldjunge91/jev-code setup

That detects the harnesses on your machine and, for each one, copies the skill and registers the Jev tool. Add harness names to be explicit (setup claude codex pi opencode), --project to install into the current repository instead of your user profile, or --dry-run to see the plan first.

3. Check it works:

npx -y @goldjunge91/jev-code doctor --live

4. Restart your agent (or /reload inside pi) and ask for something that needs a classifier:

Triage the failing tests in the last CI run: which are flaky, which are real bugs?

The agent loads the jev skill, calls jev_classify with the failures and a class set, acts on the auto results, and tells you which ones it double-checked by hand.

What a call looks like

The agent sends raw evidence and its own classes:

{
  "instructions": "Classify each test failure by its most likely root cause.",
  "items": [
    { "id": "test_login_sso", "text": "TimeoutError: SSO callback not received within 10s (attempt 3/3)" },
    { "id": "test_price_rounding", "text": "AssertionError: expected 19.99, got 19.989999999" }
  ],
  "classes": {
    "infrastructure": "Network, database, or runner problems unrelated to the code; likely passes on re-run",
    "assertion_bug": "The code produced a wrong value; deterministic and reproducible",
    "other": "Cannot tell from the excerpt"
  }
}

and gets back a label, the full distribution, and a decision it can branch on:

{
  "summary": { "items": 2, "auto": 2, "review": 0, "by_label": { "infrastructure": 1, "assertion_bug": 1 } },
  "results": [
    { "id": "test_login_sso", "label": "infrastructure", "probability": 0.93, "margin": 0.88, "confidence": 0.9, "decision": "auto", "probabilities": { "infrastructure": 0.93, "assertion_bug": 0.05, "other": 0.02 } },
    { "id": "test_price_rounding", "label": "assertion_bug", "probability": 0.97, "margin": 0.95, "confidence": 0.95, "decision": "auto", "probabilities": { "infrastructure": 0.01, "assertion_bug": 0.97, "other": 0.02 } }
  ],
  "thresholds": { "auto_accept": 0.85, "min_margin": 0.5 },
  "model": "typesafe/jev-1.13",
  "usage": { "input_tokens": 310, "output_tokens": 18 }
}

More payloads in examples/ and the full contract in skills/jev/references/tools.md.

The tools

Every tool validates its input locally (shapes, duplicate ids, request size) before spending a call, batches every item into one request, and returns decisions computed from thresholds you can override per call. Policy stays in your hands; Jev supplies the probabilities.

Per-harness details

claude plugin marketplace add goldjunge91/jev-code
claude plugin install jev-code@jev-code

npx skills add goldjunge91/jev-code --skill jev

CLI

jev-code setup [claude|codex|pi|opencode ...] [--project] [--dry-run] [--no-env]
jev-code doctor [--live]
jev-code classify --input payload.json      # same JSON as the tool
echo '{"state":"12 passed, 0 failed","checks":{"green":"Did every test pass?"}}' | jev-code check
jev-code rank --input candidates.json --pretty
jev-code mcp                                 # what the harness configs launch
jev-code skill                               # path of the bundled skill

Output is JSON on stdout. Exit code 2 means a usage or configuration problem, 1 an API failure.

Configuration

Jev runs through OpenRouter's System One endpoint. The endpoint and model are fixed.

Security notes

• Only the payload you pass reaches Jev on OpenRouter: the items, the questions, and the optional context. Nothing is read from your repository or session on its own.
• Some harnesses filter the shell environment before launching MCP servers. setup therefore copies OPENROUTER_API_KEY into the harness's own server configuration when the variable is set. Pass --no-env to skip that and rely on the runtime environment instead.
• Config files that already exist are backed up next to the original (*.bak-<timestamp>) before they are modified. Malformed JSON or TOML is left untouched and reported.
• doctor prints a masked key hint only; the key itself is never logged.

How it works

src/tools/ holds the single definition of each tool: a zod schema, a description, and a run function that builds one System One request and maps the answers to decisions. The MCP server (src/mcp/), the Pi extension (integrations/pi/), the OpenCode custom tool (integrations/opencode/), and the CLI (src/cli/) are thin adapters over that layer, which is why the payloads and results are identical everywhere. src/setup/ knows where each harness reads skills and MCP configuration and prefers each harness's own CLI over editing files.

Development

git clone https://github.com/goldjunge91/jev-code && cd jev-code
npm install
npm run check          # lint, typecheck, skill validation, tests with coverage, build, smoke
npm test               # unit tests, no API key needed
OPENROUTER_API_KEY=... npm run test:e2e # a few live calls against the real API

Try your local build against a real harness without publishing:

npm run build
node dist/cli.js setup claude --command "node $PWD/dist/cli.js mcp"
node dist/cli.js setup pi --pi-source "$PWD"

See CONTRIBUTING.md for conventions and the release process.

Related

• typesafe-ai/skills: TypeSafe's own skill for building products on Jev. jev-code is about using Jev inside the coding agent; its building-with-typesafe reference adapts the official skill's guidance (MIT, TypeSafe AI) for that case.
• jkudish/jev-mcp and itsmostafa/typesafe-mcp: other MCP servers for Jev, with different tool sets.
• TypeSafe docs and the llms.txt index.

License

MIT © François Chastel. skills/jev/references/building-with-typesafe.md adapts the TypeSafe agent skill, © 2026 TypeSafe AI, MIT. Jev and TypeSafe are trademarks of TypeSafe AI; this project is not affiliated with TypeSafe.

This fork is based on jev-code by François Chastel; the original MIT license and copyright notice are preserved.