How to Build a Claude-Powered Writing Style Checker

Building a Claude-powered writing style checker means sending text to Claude with a prompt that defines your style rules, then parsing a structured response that flags tone, clarity, voice, and consistency issues. Unlike basic grammar checkers that rely on fixed rule sets, Claude can understand nuanced style guides — brand voice, technical tone, inclusive language — and apply them contextually across long documents.

This guide walks through the actual architecture: how to design the prompt, how to get consistent structured output you can render in a UI, and how to wire it up to a production API so it's fast enough for a real editing workflow.

What a style checker actually needs to do

Before writing any code, define what "style" means for your product. Most style checkers combine a few distinct checks:

• Tone consistency — formal vs casual, matches a brand voice doc
• Clarity — sentence length, passive voice, jargon density
• Terminology — preferred terms vs banned terms (e.g., "sign up" not "signup")
• Voice — first person vs third person, active vs passive
• Readability — grade level, paragraph structure

Claude can evaluate all of these in a single pass if you give it a clear rubric and ask for structured output rather than free-form prose. Free-form feedback is hard to render in a UI and hard to diff against previous runs — structured JSON is what you want.

Step 1: Define your style guide as input, not as a hardcoded prompt

Hardcoding your style rules into the prompt works for a demo, but a real product needs per-customer or per-document style guides. Pass the style guide as a variable:

const styleGuide = `
- Use active voice.
- Avoid words: "utilize", "leverage", "synergy".
- Second person ("you"), not third person.
- Max 20 words per sentence where possible.
- Friendly but precise tone, no exclamation marks.
`;

const text = documentText;

This lets one checker serve many teams, each with their own voice, without touching code.

Step 2: Design the prompt for structured output

Ask Claude to return JSON with specific fields — issue type, location (quote the offending sentence), severity, and a suggested fix. This is the part most implementations get wrong: if you ask for "feedback" you get an essay. Ask for a schema.

You are a writing style checker. Apply the style guide below to the document.
Return ONLY valid JSON matching this schema:

{
  "issues": [
    {
      "type": "tone" | "clarity" | "terminology" | "voice" | "readability",
      "quote": "the exact sentence or phrase from the document",
      "severity": "low" | "medium" | "high",
      "suggestion": "a rewritten version"
    }
  ],
  "overall_score": 0-100
}

Style guide:
{styleGuide}

Document:
{text}

Claude is reliable at following this kind of schema when you're explicit about "ONLY valid JSON" and give a concrete example shape. For longer documents, chunk the text into sections (e.g., by heading or every ~1000 words) and run the check per chunk, then merge results — this keeps latency predictable and avoids truncated output.

Step 3: Call the API

Once the prompt is ready, the implementation is a standard API call. If you're building this as a product feature rather than a one-off script, you want an API key you can rotate, usage metadata per request, and streaming for longer documents so the UI doesn't sit blank. That's exactly what SubToAPI gives you on top of your existing Claude access — a clean sub_live_... key, a single HTTPS endpoint, and per-key usage tracking, which matters once you're billing this feature per customer.

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5",
    "max_tokens": 2000,
    "messages": [
      { "role": "user", "content": "<the prompt from step 2>" }
    ]
  }'

The quickstart covers authentication and your first request if you haven't set up a key yet, and the Messages docs cover request parameters in detail.

Step 4: Stream for long documents

A 3,000-word document generates a lot of issues, and users shouldn't wait for the full response before seeing anything. Use streaming so issues render as they're found:

const response = await fetch("https://api.subtoapi.app/v1/messages", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "claude-sonnet-4-5",
    max_tokens: 4000,
    stream: true,
    messages: [{ role: "user", content: prompt }]
  })
});

for await (const chunk of response.body) {
  // parse SSE chunks and append to a partial JSON buffer
}

Since you're expecting JSON output, you'll need to buffer the stream and parse once a complete JSON object is detected, rather than rendering raw tokens. Details on event formats are in the streaming docs.

Step 5: Add tool use for structural checks

For things that benefit from deterministic logic — word counts, sentence length, flagged terms — it's often more reliable to let Claude call a tool rather than count tokens itself in natural language. Define a check_sentence_length or flag_terms tool that your backend executes, and let Claude decide when to invoke it alongside its qualitative review. This hybrid approach (LLM judgment + deterministic tools) produces more consistent severity scoring than pure prompting. See the tools docs for the request format.

Step 6: Render and iterate

Once you have structured issues back, render them as inline highlights with hover suggestions — this is the UX pattern users expect from tools like Grammarly or Vale, but with your custom style guide instead of generic grammar rules. Let users accept/reject suggestions, and log which suggestions get accepted — this data helps you tune the style guide prompt over time without retraining anything.

Pricing and scaling considerations

A style checker that runs on every save or every PR description can generate significant request volume. Budget for this with a plan that matches your team size — SubToAPI's pricing starts at €9/month for solo use and scales to team seats at €19 or €49/seat for larger workflows with more usage and more collaborators needing their own keys.

questions

Does Claude need fine-tuning to check writing style? No. Style guides work well as prompt context — you don't need fine-tuning or embeddings for most style-checking use cases, just clear rules and a structured output schema.

How do I handle very long documents without hitting token limits? Chunk the document by section or fixed word count, run the style check per chunk, and merge the JSON results client-side. This also keeps latency predictable.

Can this distinguish between grammar errors and style preferences? Yes, if you separate them in the prompt schema. Ask Claude to tag issues by type (grammar, tone, terminology, etc.) so your UI can filter or weight them differently.