content-similarity-checker

Compare text similarity using TF-IDF, cosine similarity, and Jaccard index.

86|18|Updated Dec 14, 2025

Search Tags:#nlp #document analysis #duplicate detection #similarity #plagiarism #text comparison

Ready to run

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill content-similarity-checker

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: content-similarity-checker
Source: https://github.com/dkyazzentwatwa/chatgpt-skills/tree/main/content-similarity-checker
Command: npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill content-similarity-checker

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scikit-learn, nltk, numpy, pandas, and includes scripts (resource) components.

What problem does it solve?

This Skill helps identify how alike two pieces of text or documents are, which is crucial for tasks like plagiarism detection or finding duplicate content.

Core Features & Use Cases

Multiple Algorithms: Supports Cosine, Jaccard, Levenshtein, and TF-IDF methods for comprehensive comparison.
Batch Processing: Can compare multiple files within a folder or a list of documents against each other.
Use Case: You have a folder of articles and want to find any that are too similar to each other to be considered unique submissions.

Quick Start

Use the content-similarity-checker skill to compare the similarity between the text in 'document_a.txt' and 'document_b.txt'.

Frequently Asked Questions about content-similarity-checker

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I compare document similarity for plagiarism detection?▼

You can compare document similarity for plagiarism detection by applying TF-IDF vectorization and algorithms like cosine similarity or Jaccard index to identify matching text patterns between files.

What's the best way to find duplicate content in a folder of articles?▼

To find duplicate content in a folder of articles, use batch processing capabilities that compare multiple documents against each other to flag pairs with high text similarity scores.

How does TF-IDF and cosine similarity work for text comparison?▼

TF-IDF converts text documents into numerical vectors based on term frequency, and cosine similarity measures the angle between those vectors to quantify their similarity for text comparison.

Can I use scikit-learn and nltk for batch text similarity processing?▼

Yes, you can use scikit-learn and nltk for batch text similarity processing, leveraging scikit-learn for vectorization and matrix operations alongside nltk for tokenization across multiple files.

What is the difference between Jaccard index and Levenshtein methods for document analysis?▼

The Jaccard index measures similarity based on shared unique tokens between documents, while Levenshtein calculates the edit distance required to transform one text string into another.

Do I need pandas and numpy to run text comparison scripts?▼

Yes, you need pandas and numpy to run text comparison scripts because they handle the underlying matrix operations and data structures required for vectorization and similarity calculations.