Automate feature engineering for tabular data into predictive signals.
npx skills add https://github.com/Phife726/ds_agent --skill ds-feature-engineering
Please help me install this Agent Skill. Skill: ds-feature-engineering Source: https://github.com/Phife726/ds_agent/tree/main/ds-feature-engineering Command: npx skills add https://github.com/Phife726/ds_agent --skill ds-feature-engineering
Feature engineering is the craft of transforming raw data into predictive signals that improve model performance. It's the bridge between EDA and modeling, enabling models to learn from meaningful, domain-aware features.
Provide a feature engineering plan for a tabular dataset to improve model performance.
High-intent search queries and answers about installing and using this skill.
Automate feature engineering for tabular data by applying encoding strategies, extracting datetime features, and building ratio and interaction features to transform raw columns into predictive signals during data prep.
Extract datetime features and encode categorical data by applying targeted encoding strategies and time-based extraction techniques, turning raw columns into meaningful predictive signals that improve model performance.
Build aggregation and interaction features for per-entity modeling by generating RFM-like features and cohort-level signals across entities, bridging the gap between EDA and modeling.
Prevent data leakage when creating a feature engineering pipeline by applying best practices to avoid leakage and ensuring reproducible feature pipelines during data preparation.
Yes, you can use this feature engineering approach for data prep between EDA and modeling, as it specifically transforms raw data into domain-aware predictive signals to improve model performance.
No specific dependencies or components are required, as it operates independently while optionally referencing scripts and assets directories to organize tooling according to the Skill Unit standard.
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