Agent skill · software engineering · jeremylongshore
building-automl-pipelines
Build automated machine learning pipelines with feature engineering,
Why this skill is useful
Adds a complete Python implementation for building AutoML pipelines, including data preprocessing, model training, and hyperparameter tuning scripts.
What it needs
Requires auto-sklearn, h2o-automl, pycaret, python, tpot installed locally. About 1k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
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 1. Identify problem type (binary/multi-class classification, regression, etc.) 2. Define evaluation metrics (accuracy, F1, RMSE, etc.) 3. Set time and resource budgets for AutoML search 4. Specify feature types and preprocessing needs 5. Determine model interpretability requirements 6. Load training data using Read tool 7. Perform initial data quality assessment 8. Configure train/validation/test split strategy 9. Define feature engineering transformations 10. Set up data validation checks 11. Initialize AutoML pipeline with configuration See ${CLAUDESKILLDIR}/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 ${CLAUDESKILLDIR}/references/errors.md for comprehensive error handling. Examples See ${CLAUDESKILLDIR}/references/examples.md for detailed examples. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/building-automl-pipelines--d0a8c0