Text Detection

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

Security Scan

The skill's instructions match its stated purpose (AI-text detection) but conflict with its published metadata and ask users/agents to run npm installs of unverified packages with no source/homepage — this mismatch and runtime install requirement is suspicious and warrants caution.

Purpose & Capability

The name and description (text/AI-generated detection) align with the components the SKILL.md lists: NLP toolkit, GPT-pattern analyzer, pattern matcher, classifiers, hashing and optional fact-checker. The listed capabilities (perplexity, pattern matching, classifier integration) are coherent with a text-detection skill.

Instruction Scope

SKILL.md explicitly instructs the agent/operator to run npm install commands and import runtime packages (e.g., npm install @clawhub/nlp-toolkit). It does not instruct reading unrelated files or secrets, but it assumes the runtime can install and import node packages and execute JS. The instructions also refer to configuration and caching, but provide no runtime sandboxing guidance. Because the registry metadata lists no required binaries or install steps, this discrepancy is concerning: the instructions expect npm access but the skill metadata claims nothing is required.

Install Mechanism

There is no install spec in the registry (instruction-only), yet the SKILL.md directs installing multiple npm packages from an unverified @clawhub scope. That implies runtime downloads and code execution from the public npm registry (or other npm sources). The skill lacks source/homepage and package provenance, increasing risk: npm packages can contain arbitrary code. This is higher-risk than a purely instruction-only skill that does not call network/package installs.

Credentials

The skill declares no required environment variables, credentials, or config paths, and SKILL.md does not ask for secrets or unrelated system credentials. The absence of declared env/credentials is proportionate to the stated purpose. However, the SKILL.md's implicit need for npm and caching/storage access (to store downloaded packages and caches) is an un-declared resource requirement.

Persistence & Privilege

always is false and autonomous invocation is allowed by default (normal). The SKILL.md does not request persistent modification of system or other skills' configuration. However, because it instructs npm installs at runtime, it could cause persistent files (node_modules, caches) to be written to disk — this is not explicitly declared in the metadata and is worth noting.

What to consider before installing

This skill appears to do what it says (detect AI-generated text), but its SKILL.md expects the agent/operator to run npm installs of several @clawhub packages while the published metadata lists no install requirements or source/homepage. Before installing or enabling this skill: 1) Ask the publisher for source repositories or package URLs and verify the npm packages (@clawhub/*) exist and are trustworthy. 2) Confirm whether your agent environment permits npm installs and whether those installs are sandboxed — avoid running unvetted npm installs on production hosts. 3) Prefer a skill that provides an explicit install specification and verified package origins (GitHub releases, official project pages). 4) If you must test it, run it in an isolated sandbox/container and inspect the installed packages (review package.json, postinstall scripts) before granting broader access. 5) If you cannot verify package provenance, treat the runtime npm install requirement as a significant risk and do not enable the skill on sensitive systems.

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

---

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

---

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

---

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

---

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

{
  "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"
  }
}

---

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 and SKILLS_MANAGEMENT.md.