What is the 'trigger first' philosophy?

It prioritizes immediate execution without time-consuming pre-flight checks. Running the DAG is the most efficient way to surface actual errors, which are then diagnosed reactively.

Why are MCP tools used instead of the Airflow CLI?

MCP tools provide structured, machine-readable JSON responses. This is far more reliable for AI agents than parsing unstructured, fragile text output from CLI commands like 'astro dev run'.

Can this skill fix DAGs that fail to load?

Yes. If a DAG cannot be triggered due to syntax or dependency issues, the skill uses the 'list_import_errors' tool to find and fix the specific code preventing the DAG from loading.

How does this skill differ from simple DAG execution?

While simple skills just trigger a run, this skill is designed for iterative cycles—testing the DAG, identifying failure root causes, applying code fixes, and retesting until success.

Airflow DAG Testing & Debugging

Name: Airflow DAG Testing & Debugging
Author: astronomer

byastronomer

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安全与测试

Automates iterative test-debug-fix cycles for Apache Airflow DAGs using specialized MCP tools for reliable execution and diagnosis.

关于

This skill enables Claude to manage complex Airflow DAG testing workflows by implementing an iterative test-debug-fix loop. Instead of manual troubleshooting, it uses a 'trigger first' philosophy to identify failures immediately and then utilizes structured MCP tools to retrieve task logs, diagnose upstream dependencies, and resolve import errors. By bypassing fragile CLI commands in favor of reliable JSON-based tools, it ensures high-fidelity debugging for data engineering pipelines, allowing for rapid resolution of connection issues, logic errors, and configuration gaps.

主要功能

77 GitHub stars
Automated execution via the trigger_dag_and_wait protocol
Deep-dive failure diagnosis using structured task logs
Reliable environment interaction using MCP tools over CLI
Support for custom runtime configurations and DAG parameters
Iterative test-debug-fix loops for complex Airflow workflows

使用场景

Resolving Airflow import errors and syntax issues in new DAG files
Verifying data pipeline integrity after code changes or dependency updates
Troubleshooting failed task instances and upstream dependency bottlenecks

关于

主要功能

77 GitHub stars
Automated execution via the trigger_dag_and_wait protocol
Deep-dive failure diagnosis using structured task logs
Reliable environment interaction using MCP tools over CLI
Support for custom runtime configurations and DAG parameters
Iterative test-debug-fix loops for complex Airflow workflows

使用场景

Resolving Airflow import errors and syntax issues in new DAG files
Verifying data pipeline integrity after code changes or dependency updates
Troubleshooting failed task instances and upstream dependency bottlenecks