Analyze stylistic prints to attribute AI-generated text authorship.
npx skills add https://github.com/JGalego/Bag-of-Tricks --skill mugshot
Please help me install this Agent Skill. Skill: mugshot Source: https://github.com/JGalego/Bag-of-Tricks/tree/main/mugshot Command: npx skills add https://github.com/JGalego/Bag-of-Tricks --skill mugshot
This Skill identifies the likely author of AI-generated text by analyzing its stylistic fingerprints, helping users discern the source of machine-generated content.
Run the mugshot skill with the text: mugshot "Certainly! I'd be happy to help."
High-intent search queries and answers about installing and using this skill.
You can determine AI authorship by running the mugshot command on a text snippet to analyze its stylistic prints. The tool compares the extracted style against known profiles of common AI models to guess which one likely produced the content.
Stylometry for AI text detection works by profiling common AI styles and extracting stylistic prints from the provided content. It requires regex matching to analyze the text patterns and can optionally use an LLM provider to enhance the accuracy of the authorship attribution.
An LLM provider is not strictly required to attribute authorship of AI-generated text. The core analysis relies on regex matching and stylistic print comparison, but configuring an optional LLM provider will enhance the accuracy of the attribution results.
You can analyze PDF documents to find the source AI author using this tool. The skill includes dependencies for PDF processing, such as pypdf, pdfplumber, and pdf2image, allowing you to extract text from PDFs before performing the stylometric analysis.
The limitation of using stylistic prints for AI authorship attribution is that it relies on comparing text against known style profiles. Accuracy depends on the quality of the regex matching and the available profiles, making it a guessing mechanism rather than a definitive cryptographic verification.
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