Étincel: Non-Fiction Writing Connector

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Find the AI tells in your prose. Deterministically, locally, in CI.

npx etincel lint README.md

No install, no account, no model call, no network.

$ npx etincel lint drafts/announcement.md --register blog
✗ drafts/announcement.md  RED 100/100  (8 findings, 169 words, register: blog)
  Heavy AI styling. Multiple strong tells stacking up, worth a structural
  rewrite, not a word-swap pass.

  Whole-piece rhythm
    medium  uniform-paragraph-length
            6 paragraphs, most running about the same length with little
            variation between them. Vary paragraph length more.
    medium  low-burstiness
            13 sentences averaging 13 words, with little variation in length
            from one sentence to the next. Mix short sentences with long;
            allow fragments.
    medium  mechanical-register-drift
            Fragment rate and structural variety (sentence openers,
            punctuation mix) sit off where blog prose typically lands. Allow
            more sentence fragments and vary openers/punctuation more.

  Vocabulary and phrasing
    high    comprehensive  L3:C30   → thorough, complete
    high    cutting-edge   L3:C109  → newest, latest
    high    leverage       L3:C99   → use
    high    seamless       L3:C150  → smooth, easy
    high    streamline     L3:C70   → simplify, speed up

  strengths  specificity 11.4/1k · concrete:abstract 0.40 · burstiness 0.32

1 file audited, 1 at or above orange.

Étincel is two things. A deterministic audit that finds the patterns making prose read as machine-written, as a CLI, a GitHub Action, and an MCP tool. And a voice layer that trains Claude, Cursor, or any MCP client to draft in a style measured from your own writing, so the problem is smaller before you ever read the draft.

AI-drafted prose has a recognizable shape: uniform paragraphs, hedged authority, em dashes where a comma would do, closings that resolve too neatly. Étincel encodes the rules against that shape and, just as important, shows you what it found and why instead of quietly overwriting your voice. There is no model call anywhere in the audit path, and you decide what changes. You stay the author.

Does it work?

We measure it rather than assert it. Pooled AUC per register, measured by assay against labeled corpora with a fixed bootstrap seed, so any PR that drops these numbers fails CI:

Email is close to a coin flip. It's deliberately uncalibrated, and we track it anyway so an accidental regression doesn't go unnoticed. general is uncalibrated too. The calibrated registers are docs, blog, memo, and essay; use those if the number matters to you. See efficacy-baselines.json and src/data/SOURCES.md for method and provenance.

How this compares

vs. Vale + vale-ai-tells Vale is an excellent markup-aware linter and vale-ai-tells is a serious 78-rule package. They match tokens against patterns. Étincel measures shape: sentence-rhythm variance, paragraph-length uniformity, and repetition against your own previous drafts, the kind of statistical layer vale-ai-tells' own README says needs analysis beyond what token matching can do. Étincel also feeds a trained voice to the model before you draft, which no linter does. Running both is reasonable; they overlap less than you'd expect.

vs. skill files (stop-slop, avoid-ai-writing, no-slop) Those are instructions to a model, and they help. But a prompt can't verify it worked, can't produce an exit code, and can't gate a merge. Use one and run this in CI.

vs. humanizers (Undetectable.ai, QuillBot, StealthWriter) Those rewrite your text to evade detectors. Turnitin now has a dedicated AI-paraphrasing detection feature, expanded in 2025 to target humanizer output specifically. Étincel does the opposite job: it never modifies your text, and it would be a poor tool for fooling anyone.

vs. detectors (GPTZero, Pangram, Originality.ai, Copyleaks) Those answer "was a machine involved?" with a probability, over a whole document, as a verdict on authorship, in the cloud. Étincel answers "which specific patterns make this read as machine-written?" with locations and severities, locally, and never renders a verdict on who wrote something.

Install

Also listed on the official MCP registry as ai.etincel/etincel-nonfiction, so MCP-aware clients that auto-discover servers from there (VS Code's @mcp gallery, for one) find it without any of the config below.

Claude Code

/plugin marketplace add AIStoryHub/etincel
/plugin install etincel-nonfiction

Or from a local clone: /plugin marketplace add /path/to/etincel.

Claude Desktop / other MCP hosts

{
  "mcpServers": {
    "etincel-nonfiction": {
      "command": "npx",
      "args": ["etincel", "serve"]
    }
  }
}

From a local clone instead: npm install && npm run build, then point args at ["/path/to/etincel/dist/server.js"] with "command": "node".

Remote (hosted, multi-tenant)

A hosted version is also available at etincel.ai, exposing the same tools over Streamable HTTP (https://etincel.ai/api/mcp) with per-account auth instead of stdio. Point any MCP client at it directly:

{
  "mcpServers": {
    "etincel-nonfiction": {
      "url": "https://etincel.ai/api/mcp"
    }
  }
}

The hosted server isn't part of this repo; this repo is the local/stdio engine, CLI, and skill that the hosted version is built on top of.

Using it

Once installed, just ask for what you'd normally ask for, like "draft an email to the team about the delay," "write a blog post about X," or "clean up this memo," inside Claude Code or Claude Desktop. The skill picks up automatically for non-fiction prose of meaningful length. To train your own voice:

Train a style called "me" from these three emails I wrote: [paste samples]

Then either name it per-request ("write this in my voice") or set it as default:

Set my default style to "me"

Style presets

Twelve premade presets ship out of the box: six emotional tones (Direct & Warm, Executive Brief, Reflective Essayist, Founder Memo, Plainspoken Analyst, Wry & Candid) plus six use-case presets (PR Review, Code Comment, Slack Message, LinkedIn Post, Website Copy, Blog Post). Each carries formality/warmth/directness dials plus a sentence-rhythm and voice description that gets fed to the model as drafting context, not a template that fills in blanks. The server reads these from src/data/presets.json. Fork any preset into a trained voice with fork_style to make it your own.

Command-line lint

audit_text is a pure function under the hood, so it also ships as a CLI, for linting prose outside a chat client (READMEs, docs, PR descriptions in CI):

npx etincel lint 'docs/**/*.md'
npx etincel lint README.md --register docs --threshold yellow

Exits non-zero if any matched file's tier is at or above --threshold (default orange). .md/.mdx files default to the docs register automatically (suppresses the Markdown-structure false positives, since a real heading isn't a chatbot tell); pass --register to override. Add --json for a machine-readable report. Run npx etincel lint --help for the full option list.

A GitHub Action wraps the same CLI (see action.yml, and .github/workflows/lint.yml in this repo for a working example):

- uses: AIStoryHub/etincel@main
  with:
    patterns: "docs/**/*.md README.md"
    threshold: orange

Repo-local config: dictionary, instructions, and a shared team style

A team's rules don't have to live only in each person's local ~/.etincel/. Drop a .etincelrc (or .etincelrc.json / etincel.config.json) at the repo root and it's picked up automatically by the CLI and by the local (stdio) server, reviewable in code review and versioned instead of invisible and gone when someone leaves:

{
  "bannedWords": ["Acme Cloud Platform"],
  "allowedWords": ["leverage"],
  "register": "docs",
  "threshold": "orange",
  "instructions": "Always include a one-line CTA at the end.",
  "style": {
    "name": "House Voice",
    "dials": {
      "formality": 6,
      "warmth": 4,
      "directness": 7,
      "sentenceLength": 40,
      "sentenceRhythmVariance": 50,
      "paragraphVariance": 30,
      "contractionUse": 20,
      "emDashUse": 0,
      "fragmentTolerance": 10,
      "questionUse": 5,
      "entropy": 60
    }
  }
}

Read the full README on GitHub →