engineering-features-for-machine-learning
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. '
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill engineering-features-for-machine-learning --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Feature Engineering Toolkit Create, select, and transform features to improve ML model performance, handling scaling, encoding, interaction terms, and importance analysis. ## Overview leverage the feature-engineering-toolkit plugin to enhance machine learning models. It automates the process of creating new features, selecting the most relevant ones, and transforming existing features to better
What does the engineering-features-for-machine-learning skill do?
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. '
How do I install it?
Run `npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill engineering-features-for-machine-learning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From jeremylongshore/claude-code-plugins-plus-skills, a repository with 2,596 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.