feature-engineering-kit
Auto-generate features with encodings, scaling, polynomial features, and interaction terms for ML pipelines.
npx skills add majiayu000/claude-skill-registry --skill feature-engineering-kit-dkyazzentwatwa-chatgpt-skills --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 Kit Automated feature engineering with encodings, scaling, and transformations. ## Features - **Encodings**: One-hot, label, target encoding - **Scaling**: Standard, min-max, robust scaling - **Polynomial Features**: Generate interactions - **Binning**: Discretize continuous features - **Date Features**: Extract time-based features - **Text Features**: TF-IDF, word counts - **Missing Value Handling**: Imputation strategies ## CLI Usage ```bash python feature_engineering.py --data train.csv --output engineered.csv --config config.json ``` ## Dependencies - scikit-learn>=1.3.0 - pandas>=2.0.0 - numpy>=1.24.0
- Features
- CLI Usage
- Dependencies
python feature_engineering.py --data train.csv --output engineered.csv --config config.json
What does the feature-engineering-kit skill do?
Auto-generate features with encodings, scaling, polynomial features, and interaction terms for ML pipelines.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill feature-engineering-kit-dkyazzentwatwa-chatgpt-skills --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 majiayu000/claude-skill-registry, a repository with 534 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.