SignsOfAI.Core 0.1.0

✍︎ Signs of AI Writing

Try the live demo → — English & Spanish, runs 100% in your browser. No signup, and nothing leaves your device.

_{Real recording of the live demo — the score updates as you type, and every highlight comes with a suggested fix.}

A free, privacy-first toolkit for academic and writing integrity. It does two things:

1. De-AI-ify linter — flags the tells of AI-generated writing (overused vocabulary, rhetorical crutches, robotic sentence rhythm) and, for every finding, tells you how to fix it.
2. Originality checker — "did they write it, or copy it?" Compares documents against each other and surfaces the passages they share — verbatim copies, reworded paraphrases (even across languages), and a whole-cohort overview — as evidence a human judges. Not a black-box verdict.

🔒 Almost everything runs 100% in your browser. Your documents never leave your device. The only exception is the optional paraphrase check, which is strictly opt-in and clearly disclosed.

Built with .NET 10 and Blazor WebAssembly by Pedro Hernández (PeopleWorks), Microsoft MVP for .NET — for the .NET and Microsoft developer community, por y para la comunidad educativa.

Repo: https://github.com/peopleworks/SignsofAI

Both English and Spanish are supported throughout (auto-detected or selectable). The Spanish rule-pack is an original derivation of AI-writing markers for Spanish.

1. The AI-writing linter ("Analyze")

Unlike black-box detectors that only spit out a score, this is an explainable, actionable, educational linter. Paste, upload (.docx / .txt / .md), or just start typing — the 0–100 score, highlights, statistics, and per-finding fixes update as you write.

• Sentence-rhythm visualization — a per-sentence bar chart that makes burstiness visible.
• Per-finding recommendations — every flagged tell carries a concrete fix and the research behind it.
• Humanize (optional, BYOK) — connect an AI provider and rewrite the flagged text in one click. Anthropic (claude-opus-4-8, works from the browser), OpenAI / DeepSeek, Azure OpenAI, or Ollama (local, no key). Credentials live only in your browser and are sent directly to the provider.
• Before/after diff and a shareable result card (a PNG summary that never includes your text).
• Custom catalogs (BYO rules) — paste banned words or import a rule-pack JSON; merges live.
• Catalog page — a searchable library of every AI-writing sign, in both languages, ranked with an in-browser BM25 index.

_{This is the difference: not "87% AI", but which words, why they were flagged, and what to write instead.}

2. The Originality checker ("Originality")

"¿Lo escribió la IA, lo copiaste, o lo parafraseaste para esconderlo?" Drop in two or more documents — a thesis and its sources, a batch of student submissions — and see exactly what they share. The guiding principle is honest: we surface the evidence and highlight it; a human judges. We never accuse. This is not a whole-internet index like Turnitin.

• Shared-passage evidence — matches are highlighted in both documents, side by side; the headline overlap number equals exactly what you see highlighted (the evidence is the score).
• Phase B is the one feature that leaves the device. It's opt-in, disclosed in the UI, and sends only the sentences you choose to check to the PeopleWorks server. Everything else stays on your machine.
• Phase D is deliberately honest: we can't index the whole web, so instead of pretending to, we surface the passages worth checking and prepare the searches — nothing is sent anywhere until you click one. An optional automatic web search can be enabled by the server operator (see Optional server below).

_{A whole class at a glance: every document against every other, then the shared passages themselves — evidence, not an accusation.}

3. The predictability meter (optional server)

An honest reframing of perplexity. A small language model (Qwen2.5-0.5B or Microsoft Phi-4-mini, int8 ONNX) measures how predictable / generic a text's phrasing is. This is not an AI-vs-human verdict — on a labelled corpus the two overlap badly (memorized human text scores predictable too). We surface predictability honestly as one signal among many, calibrated per language. Opt-in; runs on the PeopleWorks server. The model lazily loads and idle-unloads to keep the server light.

4. Use it from other apps — MCP server

Everything above is also available to Claude Desktop and any MCP client through SignsOfAI.Mcp, a Model Context Protocol server (built on the official ModelContextProtocol SDK, stdio transport). Because the engine lives in SignsOfAI.Core — pure .NET, no browser — the server just exposes it as tools:

The first four run entirely on the machine; the last two disclose that they send text to the server (endpoint via the SIGNSOFAI_API_ENDPOINT environment variable). Point Claude Desktop at it:

It ships on NuGet as SignsOfAI.Mcp, so nothing needs building. Point Claude Desktop at it:

// %APPDATA%\Claude\claude_desktop_config.json
{ "mcpServers": { "signs-of-ai": {
  "command": "dnx",
  "args": ["SignsOfAI.Mcp", "--yes"]
}}}

Or install it as a global tool once — dotnet tool install --global SignsOfAI.Mcp — and use "command": "signsofai-mcp". See src/SignsOfAI.Mcp/README.md for details.

5. Use it as an agent skill — /signs-of-ai

Prefer to work inside your editor? skill/signs-of-ai is a drop-in Claude Code / Codex / agent skill that de-slops a draft — or judges whether text reads as AI-written — in English and Spanish. It's a human-readable distillation of the same rules.en.json / rules.es.json taxonomy, so it edits by the same rules the engine scores by. Install by pasting the repo link into your AI harness, or copy the folder into ~/.claude/skills/, then:

/signs-of-ai            <your draft>          # edit mode: rewrite + change summary
/signs-of-ai is this AI slop?  <the text>     # detect mode: quoted verdict, no rewrite

The skill deliberately never fakes a numeric score — for a calibrated 0–100 verdict, burstiness, originality, or perplexity it hands off to this engine (web app, CLI, or the MCP tools above). See skill/README.md.

Architecture

SignsOfAI.slnx
├─ src/
│  ├─ SignsOfAI.Core            # Pure C# engines (no UI/server deps)
│  │  ├─ Analyzers/             # Lexical, Pattern, Burstiness (IAnalyzer)
│  │  ├─ Originality/           # OriginalityChecker (shingles+tiling), ParaphraseFinder,
│  │  │                         #   DistinctivePhraseExtractor
│  │  ├─ Rules/Packs/           # rules.en.json, rules.es.json (embedded, community-extensible)
│  │  ├─ Text/                  # Tokenizer, sentence splitter, language detector, statistics
│  │  └─ AiWritingAnalyzer      # Public facade: Analyze(text, language)
│  ├─ SignsOfAI.Web             # Blazor WebAssembly front end (Analyze, Originality, Catalog)
│  ├─ SignsOfAI.Cli             # `dotnet tool` for CI pipelines
│  ├─ SignsOfAI.Mcp             # MCP server (stdio): the engine as tools for Claude Desktop / any client
│  └─ SignsOfAI.Perplexity.Api  # Optional ASP.NET Core server: predictability + embeddings
│     ├─ Engine/                #   OnnxPerplexityEngine, OnnxEmbeddingEngine (lazy-load + idle-unload)
│     └─ Config/                #   model profiles, calibration, embedding + web-search options
└─ tests/
   └─ SignsOfAI.Core.Tests      # xUnit (40+)

The Core engines are decoupled from the UI and server — the CLI, the Blazor app, and the API all reuse them.

Run it

dotnet run --project src/SignsOfAI.Web
# then open http://localhost:5019

Test

dotnet test

Command line & CI (dotnet tool)

The linter ships as a global tool so you can gate prose in CI:

dotnet tool install --global SignsOfAI.Cli

signsofai check README.md                 # pretty report
signsofai check article.docx --lang en    # Word documents too
signsofai check post.md --json            # machine-readable
signsofai check post.md --max-score 40    # exit 1 if it reads too much like AI → fails CI
signsofai check post.md --rules my-style.json   # your custom catalog

The analysis engine is also a library — dotnet add package SignsOfAI.Core:

var result = new SignsOfAI.Core.AiWritingAnalyzer().Analyze(text, "auto");
Console.WriteLine($"{result.OverallScore}/100 — {result.Verdict}");

Optional server (SignsOfAI.Perplexity.Api)

The client works fully on its own; this server only powers the opt-in features (the predictability meter and the Phase B paraphrase check). It's ASP.NET Core (.NET 10) hosting ONNX models with lazy-load and idle-unload so it stays light. Model files are not in git — they download on first use.

The client points at a hosted instance by default; to run your own, set the endpoint in the app's server settings and configure CORS for your origin.

Enabling the optional automatic web search (Phase D)

By default Phase D is the on-device, one-click-search experience (no key, nothing sent until you click). An operator can additionally enable an automatic web search — useful for presentations — by configuring a search provider on the server (the key never touches the browser). It stays off unless configured:

// appsettings.json (or environment variables)
"WebSearch": {
  "Enabled": true,
  "Provider": "brave",              // Brave Search API (free tier); provider-abstracted
  "ApiKey": "",                     // prefer the BRAVE_API_KEY environment variable
  "MaxPhrasesPerDoc": 8,
  "MaxResultsPerPhrase": 5
}

When enabled, the server advertises the capability and the client offers an automatic "search the web" action that reports pages containing a passage verbatim. If it's off, quota-exhausted, or errors, the UI falls back to the manual one-click searches — it never breaks.

Extending the rules

Add entries to src/SignsOfAI.Core/Rules/Packs/rules.<lang>.json — lexical rules match single word tokens, pattern rules are regexes for multi-word tells. Each sets a weight, severity, and suggestion.

Deploy

The Blazor client is a static bundle (hosts anywhere free). Included GitHub Actions:

• GitHub Pages (deploy-pages.yml) — Settings → Pages → Source: "GitHub Actions". The workflow rewrites the base href and writes an SPA 404.html fallback.
• Azure Static Web Apps (azure-static-web-apps.yml) — add the deployment token as a repo secret.

The optional server is a normal ASP.NET Core app (dotnet publish the SignsOfAI.Perplexity.Api project).

Credits

Created by Pedro Hernández — PeopleWorks, Microsoft MVP for .NET. Detection markers are grounded in linguistics research on AI stylometry — see Docs/GoogleResearch.md.