jev-code

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

What you get

Related MCP server: jev-mcp

Quick start

1. Get a key at console.typesafe.ai/keys and export it:

export TYPESAFE_API_KEY=ts_...

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

npx -y @francoischastel/jev-code setup

That detects the harnesses on your machine and, for each one, copies the skill and registers the 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 @francoischastel/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": "jev-latest",
  "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

jev-code setup claude copies the skill to ~/.claude/skills/jev/ and runs claude mcp add --scope user jev -- npx -y @francoischastel/jev-code mcp. The tools appear as mcp__jev__jev_classify and friends; the skill is /jev.

Prefer a plugin that updates itself? This repository is also a Claude Code plugin marketplace:

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

The plugin bundles the skill (/jev-code:jev) and the MCP server. Manual configuration and project-scope notes: docs/harnesses/claude-code.md.

jev-code setup codex copies the skill to ~/.agents/skills/jev/ (Codex's user-level skills directory, shared with Pi and OpenCode) and runs codex mcp add jev -- npx -y @francoischastel/jev-code mcp. Without the codex binary it appends a [mcp_servers.jev] table to ~/.codex/config.toml instead. Invoke the skill with $jev. Details: docs/harnesses/codex.md.

Pi has no MCP client, so jev-code is also a pi package whose extension registers the five tools natively. jev-code setup pi runs pi install npm:@francoischastel/jev-code and copies the skill to ~/.agents/skills/jev/. Run /reload inside pi afterwards. Details: docs/harnesses/pi.md.

jev-code setup opencode adds a local MCP entry to ~/.config/opencode/opencode.json (backing the file up first) and copies the skill to ~/.agents/skills/jev/, which OpenCode reads. A native custom-tool variant lives in integrations/opencode/jev.ts. Details: docs/harnesses/opencode.md.

The skill is a standard Agent Skills directory, so the skills.sh installer works for the 70+ agents it supports:

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

Pair it with the MCP server (npx -y @francoischastel/jev-code mcp) in your agent's MCP config, or let the agent fall back to the CLI.

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

The variable names match the official TypeSafe SDKs, so one export serves everything.

Security notes

• Only the payload you pass reaches TypeSafe: 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 TYPESAFE_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/FrancoisChastel/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
TYPESAFE_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; the two complement each other.

• 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. Jev and TypeSafe are trademarks of TypeSafe AI; this project is not affiliated with TypeSafe.