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Found 56 Skills
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
Use when "data pipelines", "ETL", "data warehousing", "data lakes", or asking about "Airflow", "Spark", "dbt", "Snowflake", "BigQuery", "data modeling"
Initialize and configure Astro/Airflow projects. Use when the user wants to create a new project, set up dependencies, configure connections/variables, or understand project structure. For running the local environment, see managing-astro-local-env.
Эксперт AutoML. Используй для automated machine learning, hyperparameter tuning и model selection.
Expert data engineering covering data pipelines, ETL/ELT, data warehousing, streaming, and data quality.
Use when running a dbt Fusion project with Astronomer Cosmos. Covers Cosmos 1.11+ configuration for Fusion on Snowflake/Databricks with ExecutionMode.LOCAL. Before implementing, verify dbt engine is Fusion (not Core), warehouse is supported, and local execution is acceptable. Does not cover dbt Core.
Use this for designing complex workflows, scheduled jobs, and task orchestration (Airflow, Prefect, Temporal, Cron, Celery).
Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.