Skill UI

Automates the creation of Airflow DAGs for ETL and streaming data workflows, offering best-practice guidance, production-ready code samples, and validation against common pipeline standards.

Guides you through building Airflow operators for data pipelines with automated best practices, production-ready code/config generation, and validation for ETL, transformation, orchestration, and streaming scenarios.

Builds AutoML pipelines by analyzing user requests, generating code for data preparation, model selection, hyperparameter tuning, validation, and metrics, then documenting artifacts for streamlined automated machine learning workflows.

Automates common CTE query builder tasks for data analytics, offering best-practice guidance, production-ready SQL snippets, and validation against standard requirements when you need help with BI, visualization, or statistical analysis.

Guides you through building Dagster data pipelines with best-practice advice, ready-to-use configurations, and validation checks for ETL, transformation, orchestration, and streaming workflows whenever the Dagster pipeline creator skill is activated.

This skill provides production patterns for implementing robust data quality validation using Great Expectations, dbt tests, and formal data contracts. It is essential for building reliable, automated, and verifiable data pipelines, ensuring data integrity throughout the entire ETL/ELT process, and establishing clear data governance rules.

Guides and automates Prefect Flow Builder tasks for data pipelines, offering best-practice patterns, production-ready code, configuration templates, and validation advice whenever you mention the tool or request pipeline help.

Automates sklearn pipeline builder tasks within ML training, activating on relevant triggers to guide data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Guides agents through validation, dry-run MCP tool usage, and monitors-as-code YAML generation so Monte Carlo monitors can be established via CLI or CI/CD without directly creating assets.