Originally published in Japanese on Zenn: Jevでプロンプトを仕分けして、Claudeのモデルを自動で切り替える

Goal 🎯

  • Stop burning Opus on "hello" and Haiku on design decisions
  • Let Jev (TypeSafe AI's classification model) classify each prompt, then automatically switch Claude Code's model and effort every turn

I built a Claude Code plugin that sends every prompt to Jev, picks a suitable model and effort, and applies them for that turn.

What is Jev? 🧩

You pass it text plus "descriptions of the choices," and it returns which choice fits, with probabilities.
You supply both the label names and the description text on every request. Those descriptions are all Jev uses to decide.

Input : state (text to classify) + criteria (label → description of that label) Output: choice (selected label) + confidence + probabilities (for every label)

There are three players:

Player Role
Jev Picks one label from the descriptions you passed
Custom plugin Builds the request, calls Jev, reads the response, sets the model
Claude Code Sends the model request with the model and effort the plugin chose

⚠️ Note: Jev only provides a classification API. Callers have to hit the raw API themselves.

Unlike /model, this plugin does not touch conversation history or memory files. The only thing that changes is the model name and effort on each request.

How 🛠️

1. What we send to Jev

Here is the request the plugin builds , shown as curl:

curl -s https://api.typesafe.ai/v1/systemone \ -H "Authorization: Bearer $TYPESAFE_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "state": "Fix the typo in the README", "model": "jev-latest", "questions": { "tier": { "type": "choice", "instructions": "How much model capability does answering this coding-assistant prompt need", "criteria": { "micro": "A greeting, a yes/no, a one-line factual question, or a short follow-up that needs no code reading", "low": "A small well-defined edit or lookup in one or two files, a rename, a simple command to run, formatting", "medium": "A feature or bug fix across several files, writing tests, explaining how part of a system works", "high": "Debugging that needs investigation across modules, a refactor touching many files, planning a multi-step change, a normal code review", "critical": "Architecture or design decisions, migrations, state-machine changes, ambiguous multi-step tasks with trade-offs", "max": "Fund-safety or security code, adversarial review, cryptography or key handling, anything where a subtle mistake loses money or leaks secrets" } } } }'

2. What comes back

This is the real response for the request above (state = "Fix the typo in the README"). HTTP 200, 0.17s, called once.

{ "model": "jev-1.14.0", "answers": { "tier": { "type": "choice", "choice": "low", "confidence": 0.9, "probabilities": { "micro": 0.08, "low": 0.92, "medium": 0.0, "high": 0.0, "critical": 0.0, "max": 0.0 } } }, "usage": { "input_tokens": 504, "output_tokens": 59 } }
  • choice: the label Jev picked. The plugin reads only answers.tier.choice; if the JSON is broken, it treats it as undefined.
  • probabilities: probabilities for every label. Here low is 0.92, micro is 0.08, and the rest are 0.
  • confidence and usage: the plugin ignores these.

Because Jev returned low, the plugin looks up ROUTES.low and sets haiku with effort high.

3. Deciding after the response

The label Jev returns is tier. The plugin maps tier through ROUTES to get the model and effort. Jev does not choose the model — the plugin does.

export const ROUTES = { micro: { model: 'haiku', effort: 'medium' }, low: { model: 'haiku', effort: 'high' }, medium: { model: 'sonnet', effort: 'high' }, high: { model: 'opus', effort: 'medium' }, critical: { model: 'opus', effort: 'high' }, max: { model: 'claude-fable-5-1', effort: 'high' }, }
tier (label from Jev) When Jev picks it (summary of criteria) Model the plugin uses (ROUTES[tier]) effort
micro Greeting, yes/no, one-line answer haiku medium
low Small edit in 1–2 files, rename, formatting haiku high
medium Multi-file feature or bugfix, writing tests sonnet high
high Cross-module investigation, large refactor, normal review opus medium
critical Design decisions, migrations, state-machine changes opus high
max Fund/security, adversarial review, crypto and keys claude-fable-5-1 high 
const route = ROUTES[tier] // e.g. "high" → { model: 'opus', effort: 'medium' } // One exception: if we would stay on Haiku past 150k context tokens, bump to Sonnet if (route.model === 'haiku' && contextTokens > 150_000) return ROUTES.medium return route // rewrite this turn's request to route.model / route.effort

If the label is missing from the table, or the Jev call fails, the plugin changes nothing — same behaviour as with no plugin at all.

Result ✅

The routing result shows up under the prompt almost instantly, and it feels great. It doesn't interrupt the UX at all.
The accuracy seems solid.
Simple tasks now land on Haiku or Sonnet, so the stress of picking a model yourself goes away and the workflow feels more efficient.

model-router routing micro → haiku

Going forward, I also think this kind of classification could help with things like prompt-injection defense and other security use cases.

As a side note, I also drew on the conversation in mozaic.fm ep.218.