A comprehensive suite of forensic tools for identifying AI-generated text through advanced NLP, pattern analysis, and machine learning classification.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install text-detection
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install text-detection using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Text Detection Skills?
The Text Detection Skills package is a sophisticated toolkit designed for developers who need to distinguish between human-authored and machine-generated content. By integrating this suite into your workflow using Openclaw Skills, you gain access to multi-layered analysis techniques including perplexity calculation, burstiness measurement, and BERT-based classification models.
This collection of tools is specifically engineered to handle the nuances of modern LLM outputs, offering specialized modules like the GPT Pattern Analyzer and the NLP Toolkit. Whether you are building content moderation systems or academic integrity tools, these Openclaw Skills provide the technical depth required for high-confidence AI detection.
Text Detection Skills Use Cases
- Identify articles and reports generated by GPT-3.5, GPT-4, or other modern language models.
- Detect ChatGPT responses in customer support logs or community forums.
- Analyze student essays and academic submissions for uniform linguistic structures.
- Filter high-volume content streams for AI-generated spam or low-quality automated posts.
- Perform content deduplication and similarity scoring using fuzzy matching and hashing.
How Text Detection Skills Works
- The text input is first processed by the NLP Toolkit to measure perplexity and burstiness, identifying the predictability of the writing.
- The GPT Pattern Analyzer scans the content for model-specific signatures and formal linguistic markers common to OpenAI models.
- The Pattern Matcher applies a regex library to identify repetitive sentence structures and uniform-length paragraph patterns.
- If enabled, the Text Classifier uses a fine-tuned BERT model to categorize the text as human, AI, or mixed content.
- The system aggregates these signals into a final confidence score, providing a detailed report on the likely origin of the text.
Text Detection Skills Setup
To begin using these capabilities, install the necessary modules via npm:
npm install @clawhub/nlp-toolkit @clawhub/gpt-analyzer @clawhub/pattern-matcher
Initialize your preferred detection stack within your Openclaw Skills configuration:
{
"nlp-toolkit": {
"models": ["perplexity", "burstiness", "entity"],
"cacheResults": true
},
"gpt-analyzer": {
"strictMode": true,
"minConfidence": 0.8
}
}
Text Detection Skills Data Schema & Taxonomy
The skills output structured metadata that allows for granular analysis of text authenticity:
| Attribute | Type | Description |
|---|---|---|
isGPT |
Boolean | True if AI-generated patterns exceed the threshold |
confidence |
Float | Probability score ranging from 0.0 to 1.0 |
perplexity |
Float | Measurement of how predictable the text is |
burstiness |
Float | Variation in sentence length and structure |
patterns |
Array | List of specific signatures detected (e.g., 'formal-tone') |
Text Detection Skills Advanced Features
- Advanced Detection Stacks that combine multiple analysis methods for maximum research-grade accuracy.
- Content Hashing for fast similarity checks and caching results to optimize performance in high-volume environments.
- Model Fingerprinting to specifically identify the version of the AI (e.g., GPT-3.5 vs GPT-4) used to generate the text.
- Sentiment and Tone analysis to cross-reference typically neutral AI voices against emotional human writing.
- Multi-class classification support to handle edge cases where text is a mix of human and machine-generated content.
SKILL.md
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