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home / skills / jeremylongshore / claude-code-plugins-plus-skills / feature-engineering-helper

feature-engineering-helper skill

/skills/07-ml-training/feature-engineering-helper

This skill helps optimize feature engineering workflows for ML training by generating production-ready configurations and best-practice guidance.

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

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

Files (1)
SKILL.md
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---
name: "feature-engineering-helper"
description: |
  Configure with feature engineering helper operations. Auto-activating skill for ML Training.
  Triggers on: feature engineering helper, feature engineering helper
  Part of the ML Training skill category. Use when working with feature engineering helper functionality. Trigger with phrases like "feature engineering helper", "feature helper", "feature".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Feature Engineering Helper

## Overview

This skill provides automated assistance for feature engineering helper tasks within the ML Training domain.

## When to Use

This skill activates automatically when you:
- Mention "feature engineering helper" in your request
- Ask about feature engineering helper patterns or best practices
- Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.

## Instructions

1. Provides step-by-step guidance for feature engineering helper
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with feature engineering helper"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml training concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Training** skill category.
Tags: ml, training, pytorch, tensorflow, sklearn

Overview

This skill automates common feature engineering tasks to accelerate ML training workflows. It guides data preparation, creates reproducible pipelines, and generates production-ready code and configurations. Use it to standardize feature creation, validation, and integration into training experiments.

How this skill works

The skill inspects dataset schema, missing values, and data types to recommend feature transformations and encodings. It generates step-by-step pipelines, sample code (Python/Pandas, scikit-learn, or PyTorch data transforms), and validation checks against common standards. It also outputs configuration fragments for experiment tracking and reproducible runs.

When to use it

  • You need to construct or standardize feature pipelines before model training
  • You want automated suggestions for transformations, encodings, or scaling
  • You must convert exploratory feature work into production-ready code
  • You want validation checks for feature quality and schema drift
  • You need configuration snippets for experiment tracking or deployment

Best practices

  • Start from a clean schema: declare types and identify key columns before transforming
  • Prefer pipeline objects (scikit-learn, PyTorch transforms) to keep preprocessing reproducible
  • Use cross-validation-safe transformations (fit on training splits only) to avoid leakage
  • Automate checks: null rates, cardinality, value distributions, and target leakage tests
  • Encode and store metadata (feature names, types, version) for reproducibility and drift monitoring

Example use cases

  • Create a reproducible preprocessing pipeline for tabular data including imputation, scaling, and categorical encoding
  • Generate code snippets and config entries for integrating features into an ML experiment run
  • Validate a dataset for feature quality: missingness, high-cardinality categorical columns, and label consistency
  • Convert exploratory transformations into production-ready scikit-learn ColumnTransformer or PyTorch dataset transforms
  • Produce lightweight documentation and metadata for each feature (type, source, transformation) to support model audits

FAQ

What inputs does the skill need to generate pipelines?

Provide a sample dataset or schema plus the target column and any columns to exclude. The skill works best with a representative sample of training data.

Can it prevent data leakage during preprocessing?

Yes. Recommendations include applying transformers only on training folds and using pipeline objects to ensure transforms are fit and applied correctly. The skill highlights common leakage risks and how to avoid them.