Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Text Detection
Analyzes text using NLP, GPT pattern detection, and regex matching to identify AI-generated content with configurable accuracy and speed.
skill openclawclawhub Free
Analyzes text using NLP, GPT pattern detection, and regex matching to identify AI-generated content with configurable accuracy and speed.
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- Tencent SkillHub
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- SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
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- Version
- 1.0.0
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- raghulpasupathi
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Documentation
Text Detection Skills
Skills for analyzing and detecting AI-generated text content.
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
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
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
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
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
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
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
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" } }
Performance Metrics
SkillSpeedAccuracyMemorynlp-toolkitMedium (500ms)High (85%)50MBgpt-analyzerFast (200ms)High (88%)20MBpattern-matcherVery Fast (<50ms)Medium (65%)5MBtext-classifierMedium (300ms)Very High (92%)100MBhash-toolkitVery Fast (<10ms)N/A1MB
Low Detection Accuracy
Enable all recommended skills Use advanced detection stack Increase minTextLength (>100 chars) Combine multiple methods and average scores
High False Positives
Increase confidence thresholds Enable strictMode Add custom pattern exclusions Test on known human text
Slow Performance
Use hash-toolkit for caching Switch to fast mode configuration Reduce enabled models Process text in background For implementation examples and architecture details, see AGENT.SPEC.md and SKILLS_MANAGEMENT.md.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md