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https://github.com/halans/ai-pattern-detection

AI pattern detection web app build with OpenSpec and Claude Code/Codex.
https://github.com/halans/ai-pattern-detection

ai-patterns genai preact slopdetector

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AI pattern detection web app build with OpenSpec and Claude Code/Codex.

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README 

          
# Slop Detector



A simple, pattern-based AI detection system that analyzes text for AI-generated content signals.



## Overview



This tool uses **regex-based pattern matching** to detect characteristic patterns in AI-generated text, without the complexity of ML models or GPU infrastructure. It's fast, transparent, and privacy-focused with zero data retention.



## Features



- **46 Detection Patterns**: Significance statements, AI meta-text, collaborative phrases, cultural clichés, AI-favored vocabulary, business jargon, data analysis phrases, and more

- **Fast Analysis**: <50ms CPU time per request

- **Privacy-First**: Zero data retention, ephemeral processing

- **Transparent**: See exactly which patterns were detected

- **No ML Required**: Simple pattern matching, no transformers or GPUs

- **Serverless**: Cloudflare Workers backend with automatic scaling

- **Chrome Extension**: Analyze text directly from any webpage via the Slop Detector side panel

- **File Uploads**: Analyze `.txt`, `.md`, or `.html` documents alongside pasted text



## Architecture



```

ai-detection/

├── backend/          # Cloudflare Workers API (TypeScript)

│   ├── src/

│   │   ├── patterns/     # Pattern registry and analyzer

│   │   ├── preprocessing/# Text normalization

│   │   ├── reporting/    # Report generation

│   │   └── index.ts      # Hono API

│   └── package.json

│

├── frontend/         # React + Vite + TailwindCSS

│   ├── src/

│   │   ├── components/   # TextInput, Results

│   │   ├── utils/        # API client

│   │   └── App.tsx

│   └── package.json

│

└── openspec/         # OpenSpec proposal and specs

```



## Quick Start



### Backend



```bash

cd backend

npm install

npm run dev -- --port 8787

```



Backend runs on `http://localhost:8787`



### Frontend



```bash

cd frontend

npm install

cp .env.example .env

npm run dev

```



Frontend runs on `http://localhost:3000`



## Pattern Detection



The system detects **46 AI writing patterns** grouped by severity:



### CRITICAL (15 points each)

- **AI self-references** - "as an AI language model", "as an AI assistant"

- **Knowledge cutoff disclaimers** - "as of my last update", "as at my latest training"



### HIGH (8 points each)

- **Collaborative phrases** - "let me know if", "I hope this helps", "would you like"

- **Significance statements** - "stands as a testament", "serves as a symbol"

- **Editorializing** - "it's important to note", "it is important to remember"

- **Placeholder templates** - "[insert example here]", "[placeholder text]"

- **Helpful closings** - "Certainly!", "Of course!", "here's a summary"

- **Data analysis jargon** - "deliver actionable insights through in-depth data analysis", "leveraging data-driven insights", "drive insightful data-driven decisions"

- **Business/tech jargon** - "bandwidth", "stakeholders", "value proposition", "scalable", "paradigm shift", "synergy", "ROI"



### MEDIUM (4 points each)

- **Cultural clichés** - "rich cultural heritage", "profound legacy", "rich historical tapestry"

- **Negative parallelisms** - "not only...but also", "not just...rather"

- **Vague attributions** - "studies show", "research suggests", "experts indicate"

- **Challenges/prospects** - "despite these challenges", "despite its challenges"

- **Worth mentioning** - "worth mentioning that", "it is worth mentioning"

- **Stock phrases** - "a testament to", "it's important to note that", "this is not an exhaustive list"

- **Communication styles** - "furthermore", "on the other hand", "as previously mentioned"

- **Action words** - "unlock the secrets", "delve into", "harness", "revolutionize", "elevate", "envision", "transcend", "galvanize"

- **Contextual phrases** - "in the world of", "in today's digital age/era", "when it comes to", "folks"

- **Conductor/music analogies** - "like a conductor", "orchestrate", "symphony"

- **Hyperbolic phrases** - "break barriers", "cannot be overstated", "unwavering"

- **Connectives** - "conversely", "along with", "amidst", "towards"

- **Empowerment verbs** - "empower", "embrace", "grasp", "hinder"

- **Deep + noun patterns** - "deep understanding", "deep insights", "deep dive"

- **Hustle and bustle** - Urban energy cliché phrase

- **Quantity phrases** - "a plethora of", "a multitude of", "a journey of"

- **Significance intensifiers** - "paramount", "pivotal", "undeniable", "demonstrates significant"

- **Broken citations** - "[citation needed]", "[source]"

- **Emoji headings** - "# 🎯 Getting Started", "## 🚀 Features"



### LOW (2 points each)

- **Ritual conclusions** - "in summary", "overall", "in conclusion"

- **Artificial ranges** - "from beginners to experts", "from design to deployment"

- **Title case headings** - "# The Complete Guide To Modern Development"

- **Em-dash spam** - Excessive use of em-dashes (—) in text



### VERY_LOW (1 point each)

- **AI-favored adjectives** - "robust", "seamless", "innovative", "holistic", "nuanced", "multifaceted", "groundbreaking", "quintessential", "visionary", "revolutionary", "paradigm-shifting"

- **AI-favored nouns** - "landscape", "realm", "expertise", "paradigm", "kaleidoscope", "epitome", "odyssey", "pinnacle", "nexus", "spectrum"

- **AI-favored verbs** - "facilitate", "underscore", "augment", "align", "maximize", "utilize"

- **AI descriptors** - "meticulous", "ever-evolving", "cutting-edge", "labyrinthine", "gossamer", "key", "valuable", "fresh perspectives"

- **Repetition patterns** - Repeated words, bigrams, or trigrams (3+ occurrences)



### INFORMATIONAL (0.2 points each)

- **AI transitional words** - "accordingly", "moreover", "nevertheless", "nonetheless", "thus", "undoubtedly", "certainly", "equally", "hence"



## Scoring



```

Total Score = Σ (pattern_weight × match_count)

Normalized Score = min(100, Total Score)



Classification Thresholds:

- 0-34:   Likely Human

- 35-64:  Mixed/Uncertain

- 65-100: Likely AI Slop

```



## API



### `POST /api/analyze`



**Request:**

```json

{

  "text": "Your text to analyze here..."

}

```



**Response:**

```json

{

  "classification": "Likely AI Slop",

  "confidence_score": 75,

  "patterns_detected": [...],

  "explanation": "...",

  "metadata": {

    "character_count": 1500,

    "word_count": 250,

    "pattern_engine_version": "1.2.0",

    "analysis_duration": 45,

    "timestamp": "2025-10-13T...",

    "warnings": [],

    "submission_source": "text"

  }

}

```



### `POST /api/analyze/file`



Accepts `multipart/form-data` payloads containing a single `file` field. Supported extensions: `.txt`, `.md`, `.html`.



**Response (excerpt):**



```json

{

  "classification": "Mixed/Uncertain",

  "confidence_score": 42,

  "metadata": {

    "character_count": 4800,

    "word_count": 820,

    "pattern_engine_version": "1.4.0",

    "analysis_duration": 58,

    "timestamp": "2025-10-17T...",

    "warnings": [],

    "submission_source": "file",

    "file_metadata": {

      "name": "notes.md",

      "type": "md",

      "character_count": 4700

    }

  }

}

```



## Privacy



- **Zero Data Retention**: No text is stored

- **Ephemeral Processing**: All processing in memory

- **No Logging**: Text content never logged

- **GDPR/CCPA Compliant**: No personal data collected

- **Privacy Policy**: [slopdetector.me/privacy](https://slopdetector.me/privacy)



## Performance



- **Target**: <50ms CPU time per request

- **Typical**: 20-40ms for 1000-word text

- **Max Input**: 20,000 characters



## Deployment



### Backend (Cloudflare Workers)



```bash

cd backend

npm run deploy

```



### Frontend (Cloudflare Pages)



```bash

cd frontend

npm run build

# Deploy dist/ to Cloudflare Pages

```



## Development



### Backend Structure

- `patterns/registry.ts` - Pattern definitions

- `patterns/analyzer.ts` - Pattern matching engine

- `preprocessing/normalizer.ts` - Text normalization

- `reporting/generator.ts` - Report generation

- `index.ts` - Hono API routes



### Frontend Structure

- `components/TextInput.tsx` - Text input with validation

- `components/Results.tsx` - Results visualization

- `utils/api.ts` - API client

- `App.tsx` - Main application



## Testing



### Backend Tests



The backend includes comprehensive test coverage using Vitest:



```bash

cd backend

npm test                  # Run all tests

npm run test:coverage     # Run tests with coverage report

```



**Test Suites:**

- **Pattern Registry Tests** (`registry.test.ts`) - 22 tests

  - Pattern structure validation

  - Pattern matching accuracy

  - Helper function correctness

  - Version validation



- **Analyzer Tests** (`analyzer.test.ts`) - 18 tests

  - Text analysis accuracy

  - Score calculation

  - Classification thresholds

  - Performance benchmarks



- **API Endpoint Tests** (`index.test.ts`) - 15 tests

  - Request/response validation

  - Error handling

  - Input validation

  - CORS configuration

  - Performance testing



**Total: 55+ backend tests**



### Frontend Tests



Frontend testing can be added using Vitest + React Testing Library:



```bash

cd frontend

npm test

```



### Running Specific Tests



```bash

# Run only pattern registry tests

npm test -- --run registry.test.ts



# Run only analyzer tests

npm test -- --run analyzer.test.ts



# Run only API tests

npm test -- --run index.test.ts



# Run with watch mode (development)

npm test

```



### Test Configuration



The tests use Vitest with a custom configuration (`vitest.config.ts`) that:

- Uses single fork mode to prevent memory issues

- Sets appropriate timeouts for pattern matching tests

- Enables Node environment for backend testing



### Known Issues & Workarounds



**Memory Issues:** Due to the comprehensive regex pattern matching (46 patterns), tests may encounter memory limits when run all together.



**Recommended approach:**

```bash

# Run test files individually

npm test -- --run registry.test.ts

npm test -- --run analyzer.test.ts

npm test -- --run index.test.ts



# Or increase Node memory limit

NODE_OPTIONS="--max-old-space-size=4096" npm test -- --run

```



**Why this happens:** The pattern analyzer processes text against 46 complex regex patterns. While individual file tests work fine, running all tests concurrently can exceed default memory limits.



**Production impact:** None - the API runs efficiently in Cloudflare Workers with proper memory management. This only affects comprehensive test execution.



## OpenSpec



This project follows the OpenSpec proposal workflow. See `openspec/changes/add-ai-detection-tool/` for:

- `proposal.md` - Project rationale and requirements

- `design.md` - Technical decisions and architecture

- `tasks.md` - Implementation checklist

- `specs/` - Detailed specifications for each capability



## License



MIT



## Contributing



Pattern contributions welcome! To add new patterns:



1. Add pattern to `backend/src/patterns/registry.ts`

2. Assign appropriate severity and weight

3. Test against AI and human samples

4. Submit PR with examples



## Authors



JJ Halans



## Version



1.7.0 - Pattern Engine (46 patterns - Adds undue notability coverage detection with expanded documentation/tests)

## Chrome Extension



The repository includes a Chrome side panel extension under `browser-extension/`.



### Build & Load



```bash

cd browser-extension

npm install

# Optional: override default API base URL (defaults to https://api.slopdetector.me)

export VITE_EXTENSION_API_URL="https://your-worker-domain"  # base URL only

npm run build

```



Load the generated `browser-extension/dist` folder via `chrome://extensions` (Developer Mode → Load unpacked).



### API Usage



- The extension sends a `POST ${BASE_URL}/api/analyze` request with JSON `{ "text": "..." }`.

- Ensure your backend exposes the `/api/analyze` route and accepts POST requests with raw text.

- If you override `VITE_EXTENSION_API_URL`, provide the base origin only (e.g., `https://your-worker-domain`) and the code will append `/api/analyze` when making requests.

- Failed responses surface in the panel as “Analysis failed” with the backend error message if available.