Does this skill work with LLMs other than Claude?

Yes, while designed for the Claude ecosystem, the underlying patterns use standard Python libraries, making it compatible with GPT-4, Gemini, and other major LLM providers.

What Python libraries are required for this skill?

This skill utilizes pandas, numpy, and scikit-learn. You should ensure these are installed in your local Python environment where the code will execute.

Can it handle large datasets?

The skill's performance is limited by your local machine's memory (RAM). It is highly efficient for most standard datasets used in data science and analysis.

Is my data sent to a third-party server for transformation?

No. Unlike some cloud-based AI tools, this skill generates code that executes locally on your machine, keeping your sensitive data within your own environment.

Universal Data Transformation

Name: Universal Data Transformation
Author: Microck

byMicrock

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Data Science & ML

Transforms, cleans, and reshapes complex datasets locally using industry-standard Python libraries like pandas and numpy.

About

The Universal Data Transformation skill empowers Claude and other LLMs to perform sophisticated data manipulation tasks directly within your local environment. By leveraging Python's powerhouse libraries—pandas, numpy, and scikit-learn—it provides a comprehensive toolkit for data cleaning, feature engineering, and normalization. Whether you are prepping raw CSVs for machine learning or restructuring complex dataframes for analysis, this skill ensures consistent, reproducible results across any AI provider without relying on cloud-hosted processing, ensuring your data stays secure and local.

Key Features

Universal LLM compatibility including Claude, GPT, Gemini, and DeepSeek
Advanced feature engineering with one-hot encoding and binning
Efficient data reshaping between wide and long formats using melt and pivot
Comprehensive data cleaning including outlier removal and duplicate handling
81 GitHub stars
Standardized scaling and normalization using scikit-learn methods

Use Cases

Preprocessing raw datasets for machine learning model training and evaluation
Cleaning and merging disparate data sources for business intelligence reports
Transforming bioinformatics data such as gene expression matrices and AnnData objects

About

Key Features

Universal LLM compatibility including Claude, GPT, Gemini, and DeepSeek
Advanced feature engineering with one-hot encoding and binning
Efficient data reshaping between wide and long formats using melt and pivot
Comprehensive data cleaning including outlier removal and duplicate handling
81 GitHub stars
Standardized scaling and normalization using scikit-learn methods

Use Cases

Preprocessing raw datasets for machine learning model training and evaluation
Cleaning and merging disparate data sources for business intelligence reports
Transforming bioinformatics data such as gene expression matrices and AnnData objects