building-automl-pipelines

Development

Build automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline".

QUICK START

How to use this skill

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/HEAD/plugins/ai-ml/automl-pipeline-builder/skills/building-automl-pipelines/SKILL.md

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Building Automl Pipelines

Overview

Build an end-to-end AutoML pipeline: data checks, feature preprocessing, model search/tuning, evaluation, and exportable deployment artifacts. Use this when you want repeatable training runs with a clear budget (time/compute) and a structured output (configs, reports, and a runnable pipeline).

Prerequisites

Before using this skill, ensure you have:

Python environment with AutoML libraries (Auto-sklearn, TPOT, H2O AutoML, or PyCaret)
Training dataset in accessible format (CSV, Parquet, or database)
Understanding of problem type (classification, regression, time-series)
Sufficient computational resources for automated search
Knowledge of evaluation metrics appropriate for task
Target variable and feature columns clearly defined

Instructions

Identify problem type (binary/multi-class classification, regression, etc.)
Define evaluation metrics (accuracy, F1, RMSE, etc.)
Set time and resource budgets for AutoML search
Specify feature types and preprocessing needs
Determine model interpretability requirements
Load training data using Read tool
Perform initial data quality assessment
Configure train/validation/test split strategy
Define feature engineering transformations
Set up data validation checks
Initialize AutoML pipeline with configuration

See ${CLAUDE_SKILL_DIR}/references/implementation.md for detailed implementation guide.

Output

Complete Python implementation of AutoML pipeline
Data loading and preprocessing functions
Feature engineering transformations
Model training and evaluation logic
Hyperparameter search configuration
Best model architecture and hyperparameters

Error Handling

See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive error handling.

Examples

See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed examples.

Resources

Auto-sklearn: Automated scikit-learn pipeline construction with metalearning
TPOT: Genetic programming for pipeline optimization
H2O AutoML: Scalable AutoML with ensemble methods
PyCaret: Low-code ML library with automated workflows
Automated feature selection techniques