Text Detection

Analyzes text using NLP, GPT pattern detection, and regex matching to identify AI-generated content with configurable accuracy and speed.

Text Detection Skills

Skills for analyzing and detecting AI-generated text content.

Required Skills

1. NLP Toolkit

Skill ID: nlp-toolkit Purpose: Advanced natural language processing for text analysis

Features:

• Perplexity calculation
• Sentence structure analysis
• Entity extraction
• Language detection
• Burstiness measurement

Installation:

bash

npm install @clawhub/nlp-toolkit

Configuration:

javascript

{
  "skill": "nlp-toolkit",
  "settings": {
    "models": ["perplexity", "entity", "language"],
    "cacheResults": true,
    "timeout": 5000
  }
}

Usage:

javascript

import { analyzeText } from '@clawhub/nlp-toolkit';

const result = await analyzeText(content);
// {
//   perplexity: 45.2,
//   burstiness: 0.65,
//   entities: ['GPT', 'AI'],
//   language: 'en',
//   complexity: 'medium'
// }

Use Cases:

• Measure text predictability
• Detect AI writing patterns
• Analyze sentence complexity
• Identify language and entities

Troubleshooting:

• If slow, enable caching
• For long text, split into chunks
• Language detection requires >100 chars

Related Skills: pattern-matcher, gpt-analyzer

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2. GPT Pattern Analyzer

Skill ID: gpt-analyzer Purpose: Detect GPT-specific writing patterns

Features:

• GPT-3.5/4 signature detection
• Common phrase identification
• Uniform structure detection
• Model fingerprinting

Installation:

bash

npm install @clawhub/gpt-analyzer

Configuration:

javascript

{
  "skill": "gpt-analyzer",
  "settings": {
    "models": ["gpt-3.5", "gpt-4"],
    "strictMode": false,
    "minConfidence": 0.7
  }
}

Usage:

javascript

import { detectGPT } from '@clawhub/gpt-analyzer';

const result = await detectGPT(text);
// {
//   isGPT: true,
//   confidence: 0.85,
//   modelVersion: 'gpt-3.5',
//   patterns: ['uniform-length', 'formal-tone']
// }

Use Cases:

• Identify GPT-generated articles
• Detect ChatGPT responses
• Analyze essays and reports

Troubleshooting:

• High false positives? Increase minConfidence
• Missing detections? Disable strictMode
• Check model version matches expected output

Related Skills: nlp-toolkit, pattern-matcher

---

3. Pattern Matcher

Skill ID: pattern-matcher Purpose: Fast pattern-based detection

Features:

• Regex pattern library
• Sentence structure matching
• Repetitive phrase detection
• Format consistency analysis

Installation:

bash

npm install @clawhub/pattern-matcher

Configuration:

javascript

{
  "skill": "pattern-matcher",
  "settings": {
    "patterns": [
      "repetitive-starts",
      "uniform-length",
      "formal-markers"
    ],
    "threshold": 3
  }
}

Usage:

javascript

import { matchPatterns } from '@clawhub/pattern-matcher';

const result = matchPatterns(text);
// {
//   matched: 5,
//   patterns: ['repetitive-starts', 'uniform-length'],
//   confidence: 0.65
// }

Use Cases:

• Quick pre-filtering
• Supplement other methods
• Real-time detection

Troubleshooting:

• Too many matches? Increase threshold
• Add custom patterns for specific use cases
• Combine with perplexity for better accuracy

Related Skills: nlp-toolkit, gpt-analyzer

---

Recommended Skills

4. Text Classifier

Skill ID: text-classifier Purpose: ML-based text classification

Features:

• BERT-based classification
• Multi-class support (AI vs human vs mixed)
• Fine-tuned on AI text datasets
• Fast inference (<200ms)

Installation:

bash

npm install @clawhub/text-classifier

Use Cases:

• High-accuracy classification
• Supplement rule-based methods
• Handle edge cases

Related Skills: nlp-toolkit

---

5. Content Hashing

Skill ID: hash-toolkit Purpose: Fast content fingerprinting and deduplication

Features:

• SHA-256, MD5, xxHash
• Fuzzy matching
• Content deduplication
• Similarity scoring

Installation:

bash

npm install @clawhub/hash-toolkit

Use Cases:

• Cache content analysis results
• Detect duplicate content
• Fast similarity checks

Related Skills: All detection skills

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Optional Skills

6. Sentiment Analyzer

Skill ID: sentiment-analyzer Purpose: Analyze text sentiment and tone

Features:

• Positive/negative/neutral classification
• Emotion detection
• Tone analysis (formal, casual, technical)

Use Cases:

• Detect AI's typically neutral tone
• Identify emotional language (more human)
• Supplement detection methods

---

7. Fact Checker Integration

Skill ID: fact-checker Purpose: Verify claims in text

Features:

• API integration with fact-checking services
• Claim extraction
• Source verification

Use Cases:

• Verify AI-generated facts
• Cross-reference claims
• Enhance trust scoring

---

Skill Combinations

Basic Detection Stack

json

{
  "skills": [
    "nlp-toolkit",
    "pattern-matcher",
    "hash-toolkit"
  ]
}

Use for: Quick, lightweight detection

---

Advanced Detection Stack

json

{
  "skills": [
    "nlp-toolkit",
    "gpt-analyzer",
    "text-classifier",
    "pattern-matcher",
    "hash-toolkit"
  ]
}

Use for: Maximum accuracy, research

---

Performance-Optimized Stack

json

{
  "skills": [
    "pattern-matcher",
    "hash-toolkit"
  ]
}

Use for: Real-time, high-volume detection

---

Skill Configuration Examples

High Accuracy Mode

javascript

{
  "nlp-toolkit": {
    "models": ["perplexity", "burstiness", "entity"],
    "minTextLength": 100
  },
  "gpt-analyzer": {
    "strictMode": true,
    "minConfidence": 0.8
  },
  "text-classifier": {
    "threshold": 0.9
  }
}

Fast Mode

javascript

{
  "pattern-matcher": {
    "patterns": ["basic"],
    "threshold": 2
  },
  "hash-toolkit": {
    "cacheEnabled": true,
    "algorithm": "xxhash"
  }
}

---

Performance Metrics

Skill	Speed	Accuracy	Memory
nlp-toolkit	Medium (500ms)	High (85%)	50MB
gpt-analyzer	Fast (200ms)	High (88%)	20MB
pattern-matcher	Very Fast (<50ms)	Medium (65%)	5MB
text-classifier	Medium (300ms)	Very High (92%)	100MB
hash-toolkit	Very Fast (<10ms)	N/A	1MB

---

Troubleshooting

Low Detection Accuracy

1. Enable all recommended skills
2. Use advanced detection stack
3. Increase minTextLength (>100 chars)
4. Combine multiple methods and average scores

High False Positives

1. Increase confidence thresholds
2. Enable strictMode
3. Add custom pattern exclusions
4. Test on known human text

Slow Performance

1. Use hash-toolkit for caching
2. Switch to fast mode configuration
3. Reduce enabled models
4. Process text in background

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For implementation examples and architecture details, see AGENT.SPEC.md and SKILLS_MANAGEMENT.md.