Does this skill require a specific LLM provider?

No, this skill is designed to work universally with Claude, GPT, Gemini, DeepSeek, and other providers because it executes standard Python code locally in your environment.

Which Python libraries are required for this skill?

The skill primarily utilizes pandas, numpy, and scikit-learn (sklearn) for high-performance data manipulation, scaling, and preprocessing.

Is my data sent to the cloud for transformation?

No, all data processing occurs locally on your machine using standard Python libraries, ensuring privacy and speed.

Can this skill handle bioinformatics data?

Yes, it includes specialized patterns for gene expression matrix transformation and converting AnnData objects to DataFrames.

Universal Data Transformation

Name: Universal Data Transformation
Author: Activer007

byActiver007

Data Science & ML

Cleans, reshapes, and preprocesses datasets locally using pandas, numpy, and sklearn for any LLM provider.

About

This skill provides a comprehensive toolkit for automated and guided data manipulation directly within your local environment. It streamlines the entire data preparation pipeline—from initial cleaning and handling missing values to advanced feature engineering and dataset merging. By leveraging industry-standard Python libraries like pandas and numpy, it allows developers and data scientists to transform raw data into analysis-ready formats while maintaining full local control, making it compatible with any large language model including Claude, GPT, and Gemini.

Key Features

Seamless reshaping between wide and long data formats
Advanced feature engineering and statistical binning
Automated data cleaning and missing value imputation
Efficient merging and joining of complex datasets
0 GitHub stars
Robust data normalization and scaling techniques

Use Cases

Reshaping complex bioinformatics datasets like gene expression matrices
Preprocessing raw CSV or Excel data for machine learning models
Automating repetitive data cleaning and filtering tasks in local development

About

Key Features

Seamless reshaping between wide and long data formats
Advanced feature engineering and statistical binning
Automated data cleaning and missing value imputation
Efficient merging and joining of complex datasets
0 GitHub stars
Robust data normalization and scaling techniques

Use Cases

Reshaping complex bioinformatics datasets like gene expression matrices
Preprocessing raw CSV or Excel data for machine learning models
Automating repetitive data cleaning and filtering tasks in local development