@french-castle/jev-code

Jev, TypeSafe's System One classifier, as a tool inside Claude Code, Codex, Pi, and OpenCode: typed classify, check, score, rank, and ask, plus one-command setup.

Packages

Package details

extension

Install @french-castle/jev-code from npm and Pi will load the resources declared by the package manifest.

$ pi install npm:@french-castle/jev-code
Package
@french-castle/jev-code
Version
0.3.0
Published
Sep 30, 2026
Downloads
162/mo · 162/wk
Author
french-castle
License
MIT
Types
extension
Size
361.8 KB
Dependencies
2 dependencies · 2 peers
Pi manifest JSON
{
  "extensions": [
    "./integrations/pi"
  ]
}

Security note

Pi packages can execute code and influence agent behavior. Review the source before installing third-party packages.

README


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) ─────────────▶ │           │ ───────────────────▶ │  Jev, hosted │
│ Codex        │ ───── MCP (stdio) ─────────────▶ │  jev-code │                      │  on TypeSafe │
│ OpenCode     │ ───── MCP (stdio) ─────────────▶ │           │ ◀─────────────────── │  OpenRouter  │
│ Pi           │ ───── native extension ────────▶ │           │  typed answers +     │  or Vercel   │
│ any shell    │ ───── jev-code CLI ────────────▶ │           │  probabilities       │  AI Gateway  │
└──────────────┘                                  └───────────┘                      └──────────────┘

What you get

Piece What it does
Five tools jev_classify, jev_check, jev_score, jev_rank, jev_ask. Same names, same JSON, in every harness.
One skill skills/jev/SKILL.md tells the agent when a task needs a classifier, how to write good classes and questions, and how to act on the answers. A reference adapted from TypeSafe's official skill covers building Jev into the user's own code. Follows the Agent Skills spec.
One-command setup jev-code setup detects Claude Code, Codex, Pi, and OpenCode on your machine and wires both the skill and the tool into each.
A CLI The same tools from bash, so the skill still works in a harness with no tool registered.

Quick start

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

npx -y @french-castle/jev-code@latest setup

That detects the harnesses on your machine and, for each one, copies the skill and registers the tool. When no API key is in your shell, it asks for one right there (hidden input). Any key works: TypeSafe (ts_...), OpenRouter (sk-or-...), or Vercel AI Gateway (vck_...); the prefix picks the host, so there is nothing else to configure.

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.

Prefer to keep the key in your environment? Export it before running setup, and it is picked up without a prompt. This is also what Pi and the CLI read, so setup prints the line to add to your shell profile after you paste a key:

export TYPESAFE_API_KEY=ts_...        # TypeSafe direct: console.typesafe.ai/keys
export OPENROUTER_API_KEY=sk-or-...   # OpenRouter: already set if you use it elsewhere
export AI_GATEWAY_API_KEY=vck_...     # Vercel AI Gateway

2. Check it works:

npx -y @french-castle/jev-code@latest doctor --live

3. 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

Tool Ask it when Comes back with
jev_classify Many items, one label each from your classes label, probabilities, margin, decision: auto | review
jev_check Yes/no questions about one piece of evidence probability, verdict: yes | no | uncertain
jev_score Many items on one ordered scale (severity, priority) score, nearest level, confidence, decision
jev_rank Which candidates answer a question relevance per candidate, sorted, plus any_relevant
jev_ask Anything else: mixed question types over one state the raw System One answers

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 @french-castle/jev-code@0.3.0 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 @french-castle/jev-code@0.3.0 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:@french-castle/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 @french-castle/jev-code@0.3.0 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] [--no-prompt]
jev-code doctor [--live]                     # which host and key are in use, what is installed
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.

Upgrading

Harness configs launch a pinned command, npx -y @french-castle/jev-code@0.3.0 mcp, so an agent starts fast and works offline once that version is cached, and nothing changes under you until you decide. To upgrade, run setup again with @latest: it re-registers the tool with the new pin, replacing the old entry (also through claude mcp and codex mcp), and refreshes the copied skill. doctor shows the pinned version of every registration and says when to re-run setup.

npx -y @french-castle/jev-code@latest setup

Installs made with 0.2.1 or earlier launch an unpinned command. npx keeps the first version it cached for an unpinned name and never looks for a newer one, so those installs do not see updates on their own. Run the command above once to move them to a pinned command.

Configuration

One API key is required. The three hosts below all serve Jev behind the same System One API, so the tools behave identically; only the account you pay through changes.

Host Key variable Key prefix Default model Get a key
TypeSafe (direct) TYPESAFE_API_KEY ts_ jev-latest console.typesafe.ai/keys
OpenRouter OPENROUTER_API_KEY sk-or- jev-latest openrouter.ai/settings/keys
Vercel AI Gateway AI_GATEWAY_API_KEY vck_ typesafe-ai/jev AI Gateway API keys

How the host is chosen:

  • The key's prefix decides, whichever variable holds it: TYPESAFE_API_KEY=sk-or-... goes to OpenRouter.
  • When several keys are set, the first row in the table wins. doctor says which one is in use.
  • JEV_CODE_PROVIDER=openrouter (or typesafe, vercel) forces a host.
  • A key is only ever sent to the host that issued it; a mismatch is refused before any request. The exact rules are in SECURITY.md.
Variable Default Purpose
JEV_CODE_PROVIDER auto Force typesafe, openrouter, or vercel.
TYPESAFE_BASE_URL per host Point at a proxy or a compatible endpoint. TypeSafe keys only, unless JEV_CODE_PROVIDER is set.
TYPESAFE_DEFAULT_MODEL per host Pin a Jev version: jev-1.13 on TypeSafe or OpenRouter; Vercel uses typesafe-ai/jev.
JEV_CODE_TIMEOUT_MS 30000 Per-attempt timeout.
JEV_CODE_MAX_RETRIES 2 Retries on 429, 5xx, timeouts, and connection errors.

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

Security notes

  • Only the payload you pass to a tool leaves your machine, and only to the host your key belongs to. Nothing is read from your repository or session on its own.
  • Keys are read from the environment, sent only to the host that issued them, and never logged. setup copies the key in use into harness configs so filtered environments still work; --no-env skips that.
  • Existing config files are backed up before setup modifies them.

Hosts, the proxy rule, what setup executes, and how to report a vulnerability: SECURITY.md.

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/core/providers.ts is the table of hosts that serve Jev; adding one that speaks the System One API is a row there. 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; any provider key works

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

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.