Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Nlp Toolkit
Advanced NLP with perplexity scoring, burstiness analysis, and entropy calculation
skill openclawclawhub Free
Advanced NLP with perplexity scoring, burstiness analysis, and entropy calculation
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0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- raghulpasupathi
- Source page
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Documentation
NLP Toolkit
Advanced NLP analysis for AI content detection using statistical measures.
Implementation
/** * Analyze text using NLP metrics * @param {string} text - Text to analyze * @param {object} options - Configuration options * @returns {object} NLP analysis results */ async function analyzeText(text, options = {}) { const { perplexityThreshold = 45.0, burstinessThreshold = 0.35, minTextLength = 50 } = options; if (text.length < minTextLength) { return { error: 'Text too short for analysis', minLength: minTextLength }; } // Calculate perplexity (simplified) const perplexity = calculatePerplexity(text); // Calculate burstiness const burstiness = calculateBurstiness(text); // Calculate entropy const entropy = calculateEntropy(text); // Token distribution analysis const tokenStats = analyzeTokenDistribution(text); // Determine if AI-generated const isAI = perplexity < perplexityThreshold && burstiness < burstinessThreshold; const confidence = calculateConfidence(perplexity, burstiness, entropy); return { isAI, confidence: Math.round(confidence * 100), metrics: { perplexity: Math.round(perplexity * 100) / 100, burstiness: Math.round(burstiness * 100) / 100, entropy: Math.round(entropy * 100) / 100 }, tokenStats, thresholds: { perplexity: perplexityThreshold, burstiness: burstinessThreshold }, explanation: isAI ? 'Low perplexity and uniform burstiness suggest AI generation' : 'Natural variation in metrics suggests human writing' }; } /** * Calculate perplexity score (simplified) */ function calculatePerplexity(text) { const words = text.toLowerCase().split(/\s+/); const uniqueWords = new Set(words); // Simplified perplexity: ratio of unique words to total // Real perplexity requires language model const ratio = uniqueWords.size / words.length; const perplexity = 100 / ratio; // Inverse relationship return Math.min(perplexity, 100); } /** * Calculate burstiness (variation in sentence length) */ function calculateBurstiness(text) { const sentences = text.split(/[.!?]+/).filter(s => s.trim()); if (sentences.length < 2) return 0; const lengths = sentences.map(s => s.split(/\s+/).length); const avg = lengths.reduce((a, b) => a + b, 0) / lengths.length; const variance = lengths.reduce((sum, len) => sum + Math.pow(len - avg, 2), 0) / lengths.length; const stdDev = Math.sqrt(variance); // Burstiness: coefficient of variation const burstiness = stdDev / avg; return Math.min(burstiness, 1.0); } /** * Calculate Shannon entropy */ function calculateEntropy(text) { const chars = text.toLowerCase().split(''); const freq = {}; // Count character frequencies for (const char of chars) { freq[char] = (freq[char] || 0) + 1; } // Calculate entropy let entropy = 0; const total = chars.length; for (const count of Object.values(freq)) { const p = count / total; entropy -= p * Math.log2(p); } return entropy; } /** * Analyze token distribution */ function analyzeTokenDistribution(text) { const words = text.toLowerCase().split(/\s+/); const uniqueWords = new Set(words); return { totalWords: words.length, uniqueWords: uniqueWords.size, vocabularyRichness: Math.round((uniqueWords.size / words.length) * 100) / 100 }; } /** * Calculate overall confidence */ function calculateConfidence(perplexity, burstiness, entropy) { // Lower perplexity = more AI-like const perplexityScore = Math.max(0, 1 - (perplexity / 100)); // Lower burstiness = more AI-like const burstinessScore = Math.max(0, 1 - (burstiness / 0.5)); // Moderate entropy expected for AI const entropyScore = (entropy > 3.5 && entropy < 5.0) ? 0.8 : 0.4; const confidence = (perplexityScore + burstinessScore + entropyScore) / 3; return Math.min(confidence, 1.0); } // Export for OpenClaw module.exports = { analyzeText, calculatePerplexity, calculateBurstiness, calculateEntropy };
Usage
const result = await skills.nlpToolkit.analyzeText(text, { perplexityThreshold: 45.0, burstinessThreshold: 0.35 }); console.log(`AI Detection: ${result.isAI} (${result.confidence}% confidence)`); console.log(`Perplexity: ${result.metrics.perplexity}`); console.log(`Burstiness: ${result.metrics.burstiness}`);
Configuration
{ "perplexityThreshold": 45.0, "burstinessThreshold": 0.35, "minTextLength": 50 }
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