Detect AI-generated text using Normalized Compression Distance against known corpora.
npx skills add https://github.com/JGalego/Bag-of-Tricks --skill squeeze
Please help me install this Agent Skill. Skill: squeeze Source: https://github.com/JGalego/Bag-of-Tricks/tree/main/squeeze Command: npx skills add https://github.com/JGalego/Bag-of-Tricks --skill squeeze
This Skill allows users to quickly determine if a piece of text is likely generated by an AI or a human, based on its compression properties.
Analyze the text in the provided document 'analysis.txt' to determine if it's likely AI-generated.
High-intent search queries and answers about installing and using this skill.
AI-generated text detection via compression analysis evaluates text authenticity by comparing its Normalized Compression Distance against known AI and human corpora. This approach provides an immediate assessment of whether content is likely machine-generated based on its compression properties.
Normalized Compression Distance (NCD) is a metric used to assess text authenticity by measuring the similarity between a target text and reference corpora through compression ratios. It identifies AI-generated content by analyzing how efficiently the text compresses compared to known human and AI samples.
Yes, you need Python's lzma, zlib, or bz2 modules to perform AI text detection with this Skill. These compression libraries are required dependencies for calculating the Normalized Compression Distance and evaluating the likelihood of text being AI-generated.
Yes, compression-based AI detection is designed for rapid evaluation of text authenticity in administrative and academic workflows. It provides a fast analysis method to process text and assess whether it is likely AI-generated with minimal delay.
To analyze a document for AI authorship, provide the text in a file like 'analysis.txt' and run it through the squeeze Skill. The tool calculates the text's compression distance against reference corpora to determine the likelihood of AI generation.
Using Normalized Compression Distance offers a straightforward way to check text authenticity without training dedicated machine learning models. By leveraging standard Python compression modules like lzma, zlib, or bz2, it compares text against known corpora to rapidly estimate AI-likeness.
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