Text Detection

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:

npm install @clawhub/nlp-toolkit

Configuration:

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

Usage:

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:

npm install @clawhub/gpt-analyzer

Configuration:

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

Usage:

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

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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:

npm install @clawhub/pattern-matcher

Configuration:

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

Usage:

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

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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:

npm install @clawhub/text-classifier

Use Cases:

High-accuracy classification

Supplement rule-based methods

Handle edge cases

Related Skills: nlp-toolkit

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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:

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

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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

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Skill Combinations

Basic Detection Stack

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

Use for: Quick, lightweight detection

---

Advanced Detection Stack

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

Use for: Maximum accuracy, research

---

Performance-Optimized Stack

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

Use for: Real-time, high-volume detection

---

Skill Configuration Examples

High Accuracy Mode

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

Fast Mode

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

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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 |

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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.*

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.*