@astronomer

deploying-airflow covers three distinct deployment paths: astro cli for managed operations, docker compose for local and dev environments, and kubernetes helm charts for production orchestration. includes ci/cd patterns and configuration examples.

debugging-dags provides structured root cause analysis for airflow dag failures using a four-step investigation workflow, failure categorization, and tool-specific commands for astro and oss deployments.

cosmos-dbt-core bridges dbt core projects and airflow through three assembly patterns and eight execution modes, requiring upfront verification of engine type, warehouse, and airflow version before implementation.

migrating-airflow-2-to-3 detects and resolves breaking changes in airflow 2.x to 3.x upgrades using ruff-based rules and a manual checklist for issues automation cannot handle.

testing-dags automates iterative test-debug-fix cycles for airflow dags with diagnostic output and validation commands, supporting timeouts and retries across multiple failure scenarios.

setting-up-astro-project covers initialization and configuration of airflow projects via astro cli, including dependency management, connection setup, and dag validation through declarative yaml and command-line tooling.

tracing-downstream-lineage helps engineers identify what breaks when altering data tables or pipelines by mapping consumer dependencies, categorizing risk, and generating impact reports before deployment.

managing-astro-local-env covers starting, stopping, and restarting local airflow containers via astro cli, viewing logs across services, accessing container shells, and troubleshooting common docker and standalone setup failures.

creating-openlineage-extractors covers building custom lineage capture for unsupported airflow operators via two patterns: direct methods on owned operators or standalone extractors for third-party code, with registration via airflow.cfg or environment variables.

warehouse-init discovers database schemas and generates a markdown reference file (.astro/warehouse.md) for schema lookup and team collaboration without repeated warehouse queries.

analyzing-data caches query patterns and table schemas to answer business questions against data warehouses, with run_sql() and run_sql_pandas() functions returning polars or pandas dataframes.

airflow — an installable skill for AI agents, published by astronomer/agents.

Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use…

troubleshooting-astro-deployments — an installable skill for AI agents, published by astronomer/agents.

managing-astro-deployments — an installable skill for AI agents, published by astronomer/agents.

Use when running a dbt Fusion project with Astronomer Cosmos. Covers Cosmos 1.11+ configuration for Fusion on Snowflake/Databricks with ExecutionMode.LOCAL.…

Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator,…

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…

Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or…

Deep-dive data profiling for a specific table. Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs…

checking-freshness — an installable skill for AI agents, published by astronomer/agents.

Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and…