Does this skill require a local Airflow installation?

No, it uses 'uvx' to run the 'astro-airflow-mcp' CLI, meaning it can interact with remote Airflow instances via the Airflow API without requiring a local installation.

Can I manage multiple Airflow environments?

Yes, the skill supports instance configuration, allowing you to list, switch between, and manage production, staging, and local environments easily.

How do I troubleshoot a failed DAG run using this skill?

You can use the 'af runs diagnose' command to get an automated summary of why a run failed and then use 'af tasks logs' to inspect the specific errors in the task logs.

What is the output format for these commands?

Most commands output JSON, which allows Claude and the user to filter and process data efficiently using tools like jq.

Does it support Airflow Variables and Connections?

Yes, you can list and retrieve Airflow variables, connections, and pools to ensure your environment is correctly configured.

Airflow Operations Manager

Name: Airflow Operations Manager
Author: astronomer

byastronomer

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Analíticas y Monitorización

Manages Apache Airflow workflows by listing, triggering, and debugging DAGs and tasks directly through Claude.

Acerca de

This skill integrates Apache Airflow management directly into the Claude interface, allowing data engineers to interact with their pipelines seamlessly. It provides a comprehensive suite of tools to explore DAG source code, monitor system health, and diagnose failed runs without context-switching. By utilizing the 'af' command suite, users can manage variables, check task logs, and control workflow execution across multiple instances from local development to production environments.

Características Principales

Real-time task log retrieval with support for retries and mapped task indexes
77 GitHub stars
Advanced run diagnostics to quickly identify and resolve pipeline failures
Comprehensive DAG management including listing, pausing, and triggering workflows
System-wide health monitoring for connections, variables, and resource pools
Multi-instance support for switching between production, staging, and local environments

Casos de Uso

Troubleshooting failed production pipelines by analyzing task logs and run diagnostics
Performing daily system health checks across multiple Airflow environments
Triggering and monitoring long-running data workflows with automated status updates

Acerca de

Características Principales

Real-time task log retrieval with support for retries and mapped task indexes
77 GitHub stars
Advanced run diagnostics to quickly identify and resolve pipeline failures
Comprehensive DAG management including listing, pausing, and triggering workflows
System-wide health monitoring for connections, variables, and resource pools
Multi-instance support for switching between production, staging, and local environments

Casos de Uso

Troubleshooting failed production pipelines by analyzing task logs and run diagnostics
Performing daily system health checks across multiple Airflow environments
Triggering and monitoring long-running data workflows with automated status updates