Can this skill handle categorical data?

Yes, it includes specific patterns for One-Hot encoding, Label encoding, and binning continuous variables into categorical labels.

Which Python libraries do I need installed?

To use this skill effectively, you should have pandas, numpy, and scikit-learn installed in your Python environment.

Is it suitable for large-scale data processing?

Since the skill executes locally, it is limited only by your machine's RAM and CPU. It uses efficient pandas and numpy operations designed for high-performance data manipulation.

Does this skill require a specific LLM provider?

No, this skill is provider-agnostic. It generates standard Python code that executes in your local environment, making it compatible with Claude, GPT-4, Gemini, and other models.

Universal Data Transformation

Name: Universal Data Transformation
Author: Microck

byMicrock

•

Data Science & ML

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

This skill provides a robust framework for comprehensive data preprocessing and feature engineering within your local environment. It equips Claude with standardized patterns for handling missing values, normalizing numeric features, and merging disparate datasets. By leveraging pandas, numpy, and scikit-learn, it enables complex operations like wide-to-long reshaping, categorical encoding, and outlier removal. It is an essential tool for developers and data scientists who need to prepare raw data for analysis or machine learning models across any LLM provider without relying on cloud-hosted transformation services.

Key Features

01Robust merging and joining capabilities for combining disparate data sources

0281 GitHub stars

03Advanced normalization and scaling techniques using scikit-learn implementations

04Comprehensive data cleaning including outlier removal and missing value imputation

05Automated feature engineering including binning, encoding, and time-series extraction

06Flexible data reshaping with multi-index pivot tables and melting operations

Use Cases

01Transforming complex bioinformatics datasets for differential expression analysis

02Cleaning and aggregating system logs or time-series data for business intelligence reporting

03Preprocessing raw CSV or Excel data for machine learning model training and evaluation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills data-transform

For use in Claude.ai and ChatGPT

Download Skill