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feature-engineering-toolkit skill

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/backups/skills-migration-20251108-070147/plugins/ai-ml/feature-engineering-toolkit/skills/feature-engineering-toolkit

This skill helps you enhance machine learning models by creating, selecting, and transforming features using a dedicated feature-engineering toolkit.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill feature-engineering-toolkit

Review the files below or copy the command above to add this skill to your agents.

Files (4)

SKILL.md

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---
name: engineering-features-for-machine-learning
description: |
  This skill empowers Claude to perform feature engineering tasks for machine learning. It creates, selects, and transforms features to improve model performance. Use this skill when the user requests feature creation, feature selection, feature transformation, or any request that involves improving the features used in a machine learning model. Trigger terms include "feature engineering", "feature selection", "feature transformation", "create features", "select features", "transform features", "improve model performance", and similar phrases related to feature manipulation.
---

## Overview

This skill enables Claude to 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 suit the model's needs. By using this skill, you can improve the accuracy, efficiency, and interpretability of your machine learning models.

## How It Works

1. **Analyzing Requirements**: Claude analyzes the user's request and identifies the specific feature engineering task required.
2. **Generating Code**: Claude generates Python code using the feature-engineering-toolkit plugin to perform the requested task. This includes data validation and error handling.
3. **Executing Task**: The generated code is executed, creating, selecting, or transforming features as requested.
4. **Providing Insights**: Claude provides performance metrics and insights related to the feature engineering process, such as the importance of newly created features or the impact of transformations on model performance.

## When to Use This Skill

This skill activates when you need to:
- Create new features from existing data to improve model accuracy.
- Select the most relevant features from a dataset to reduce model complexity and improve efficiency.
- Transform features to better suit the assumptions of a machine learning model (e.g., scaling, normalization, encoding).

## Examples

### Example 1: Improving Model Accuracy

User request: "Create new features from the existing 'age' and 'income' columns to improve the accuracy of a customer churn prediction model."

The skill will:
1. Generate code to create interaction terms between 'age' and 'income' (e.g., age * income, age / income).
2. Execute the code and evaluate the impact of the new features on model performance.

### Example 2: Reducing Model Complexity

User request: "Select the top 10 most important features from the dataset to reduce the complexity of a fraud detection model."

The skill will:
1. Generate code to calculate feature importance using a suitable method (e.g., Random Forest, SelectKBest).
2. Execute the code and select the top 10 features based on their importance scores.

## Best Practices

- **Data Validation**: Always validate the input data to ensure it is clean and consistent before performing feature engineering.
- **Feature Scaling**: Scale numerical features to prevent features with larger ranges from dominating the model.
- **Encoding Categorical Features**: Encode categorical features appropriately (e.g., one-hot encoding, label encoding) to make them suitable for machine learning models.

## Integration

This skill integrates with the feature-engineering-toolkit plugin, providing a seamless way to create, select, and transform features for machine learning models. It can be used in conjunction with other Claude Code skills to build complete machine learning pipelines.

Overview

This skill enables Claude to perform practical feature engineering to improve machine learning models. It automates creating, selecting, and transforming features using programmatic Python code and common toolkits. The skill focuses on measurable gains in accuracy, efficiency, and interpretability.

How this skill works

Claude first parses your request and inspects the dataset schema and task objective. It generates and runs Python code to create new features, apply transformations (scaling, encoding, interactions), and compute feature importance or selection. Finally, Claude reports metrics and diagnostics showing how the engineered features affect model performance and data quality.

When to use it

You need to create derived features (interaction terms, ratios, binned variables) to boost model signal.
You want to reduce feature set size by selecting the most predictive variables for training and inference efficiency.
You must transform features to meet model assumptions (scaling, normalization, log transforms).
You need to encode or impute categorical and missing data before modeling.
You want quick diagnostics showing feature importance and the impact of new features on validation metrics.

Best practices

Validate and clean input data first: check types, missingness, outliers, and duplicates.
Prefer simple, interpretable features first (ratios, bins, aggregations) before complex transformations.
Scale or normalize numeric features when using distance-based or gradient methods.
Encode categorical variables appropriately for the model (one-hot for trees optional, embeddings/target encoding for high-cardinality features).
Use cross-validation and holdout metrics to evaluate feature changes and avoid leakage.

Example use cases

Create interaction and polynomial features from age and income to improve churn prediction AUC.
Select the top N features for a production fraud model to reduce inference latency and memory use.
Apply log transforms and scaling to skewed financial variables before training a regression model.
Encode and aggregate high-cardinality categorical IDs into target-encoded features for a recommendation model.
Generate time-based features (lags, rolling means) for a time-series forecasting pipeline.

FAQ

What inputs do I need to provide?

Provide a sample dataframe or dataset schema, target column, and the modeling goal (classification/regression). Optionally list columns to keep or exclude.

How do you prevent data leakage during feature creation?

Features are created using only training folds or historical data depending on the task; cross-validation and proper time-based splits are used for validation.