Individual skill
airflow
Queries, manages, and troubleshoots Apache Airflow using the `af` CLI. Use when working with anything related to Airflow - a DAG, a DAG run, a task log, an import or parse error, a broken DAG, or any Airflow operation. Covers listing and triggering DAGs, retrying runs, reading task logs, diagnosing failures, debugging import and parse errors, checking connections, variables and pools, exploring the REST API, and monitoring health (for example "trigger a pipeline", "retry a run", "list connections", "check Airflow health", "why did my DAG fail"). This is the entrypoint that routes to sibling skills for authoring, testing, deploying, and migrating Airflow 2 to 3. Not for warehouse/SQL analytics on Airflow metadata tables (use analyzing-data); for deep root-cause reports use debugging-dags or airflow-investigation.
skills/airflow/SKILL.md
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- Last checked
- Nov 6, 2026
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- Repository stars (not skill ratings)
- 458
- Repository last updated
- Nov 2, 2026
- License
- Apache-2.0
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- agents Repository topic
- ai Repository topic
- airflow Repository topic
- data-engineering Repository topic
- mcp Repository topic
- skills Repository topic
- claude Repository topic
- cursor Repository topic
- apache-airflow Repository topic
- agentic-workflow Repository topic
- data Derived category
- testing Derived category
- data-databases Derived category
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Read excerpt
# Airflow Operations
Use `af` commands to query, manage, and troubleshoot Airflow workflows.
## Astro CLI
The [Astro CLI](https://www.astronomer.io/docs/astro/cli/overview) is the recommended way to run Airflow locally and deploy to production. It provides a containerized Airflow environment that works out of the box:
```bash
# Initialize a new project
astro dev init
# Start local Airflow (webserver at http://localhost:8080)
astro dev start