engineering-features-for-machine-learning

5.2

Execute create, select, and transform features to improve machine learning model performance. Handles feature scaling, encoding, and importance analysis. Use when asked to "engineer features" or "select features". Trigger with relevant phrases based on skill purpose.

feature engineering

5.2

Rating

Installs

Machine Learning

Quick Review

This skill provides a reasonable foundation for feature engineering tasks with clear use cases and examples. The description covers the main capabilities (create, select, transform features), and the structure includes supporting scripts referenced in the directory tree. However, the skill suffers from generic placeholder content ('appropriate file access permissions', 'structured output relevant to the task') that reduces clarity for invocation. The task knowledge appears adequate given the referenced Python scripts (feature_engineering_pipeline.py, feature_importance_analyzer.py, etc.), though SKILL.md itself lacks specific technical details about parameters or API usage. The novelty is moderate—while feature engineering does require domain knowledge, many of these tasks (scaling, encoding, importance analysis) are well-supported by existing libraries like scikit-learn, so the cost savings may be limited to orchestration and pipeline building rather than truly complex operations. The structure is acceptable but slightly cluttered with redundant sections and boilerplate text.