Gael Varoquaux expanded documentation for the skrub library within the pola-rs/polars and piotrplenik/pandas repositories, focusing on improving machine learning workflow integration across major data science ecosystems. He authored detailed Markdown guides that clarify how skrub bridges DataFrames from Polars and Pandas to scikit-learn estimators, enabling more efficient feature engineering and model training. By aligning documentation standards and demonstrating best practices, Gael enhanced the discoverability and usability of skrub for data-heavy environments. His work emphasized cross-repository collaboration and ecosystem conventions, providing clear, actionable resources for developers seeking to integrate advanced ML capabilities into their data processing pipelines.