Text Detection
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totalclaw install clawskills:raghulpasupathi~raghulpasupathi-text-detectioncURL直接下载,无需登录
curl -fsSL https://skills.taituai.com/api/skills/clawskills%3Araghulpasupathi~raghulpasupathi-text-detection/file -o raghulpasupathi-text-detection.mdGit 仓库获取源码
git clone https://github.com/openclaw/skills/commit/1ad2eefd1fd51e41cff87b9eb3c1f7b8378c9a64# 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`
---
### 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
---
## 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
---
*For implementation examples and architecture details, see [AGENT.SPEC.md](../../AGENT.SPEC.md) and [SKILLS_MANAGEMENT.md](../../SKILLS_MANAGEMENT.md).*