Equip AI agents and workflows with DataRobot skills to handle full ML lifecycle: prepare and validate datasets, engineer features, train models via AutoML, deploy prediction endpoints, generate real-time or batch predictions, monitor drift and anomalies, explain SHAP values, and automate CI/CD pipelines using Python SDK, OpenTelemetry instrumentation, GitHub Actions, GitLab CI, and Pulumi.
npx claudepluginhub datarobot-oss/datarobot-agent-skills --plugin datarobot-agent-skillsComprehensive guidance for training models in DataRobot, including project creation, AutoML configuration, feature engineering, and model selection.
Guidance for setting up CI/CD pipelines for DataRobot application templates using GitLab, GitHub Actions, and Pulumi for infrastructure as code.
Tools and guidance for data upload, dataset management, data validation, and preparing data for DataRobot projects.
Instrument any external AI agent with OpenTelemetry to send traces, logs, and metrics to DataRobot for monitoring, observability, and governance.
Guidance for feature engineering, feature discovery, feature importance analysis, and understanding DataRobot's automated feature engineering capabilities.
Tools and guidance for deploying DataRobot models, managing deployments, configuring prediction environments, and deployment operations.
Tools and guidance for model explainability, prediction explanations, feature impact analysis, SHAP values, and model diagnostics.
Tools and guidance for monitoring model performance, tracking data drift, managing model health, and detecting prediction anomalies.
Tools and guidance for making predictions with DataRobot models, including real-time predictions, batch scoring, and prediction dataset generation.
Share bugs, ideas, or general feedback.
Build AutoML pipelines
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
Agents for data engineering, machine learning, and AI development
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
ML engineering plugin: Give your AI coding agent ML engineering superpowers.