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testing-dags

Trigger Airflow DAG runs, diagnose failures, and inspect task logs.

419|61|Updated Jan 13, 2026
Search Tags:#workflow#testing#debug#airflow#pipelines#dag
One-click install
npx skills add https://github.com/astronomer/agents --skill testing-dags
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: testing-dags
Source: https://github.com/astronomer/agents/tree/main/skills/testing-dags
Command: npx skills add https://github.com/astronomer/agents --skill testing-dags

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables teams to perform iterative DAG testing in Airflow by guiding users through a repeatable test-debug-fix cycle, reducing time to identify and fix issues in DAGs.

Core Features & Use Cases

  • Iterative testing workflow: trigger a DAG run, observe results, and loop through debugging and fixes.
  • Comprehensive diagnostics: diagnose runs, fetch task logs, and surface root causes.
  • Use Case: when validating a new DAG or troubleshooting a failing schedule, this skill streamlines testing and remediation.

Quick Start

Use the skill to iteratively test a DAG by triggering a run, inspecting outcomes, and applying fixes. Trigger a test run: uvx --from astro-airflow-mcp@latest af runs trigger-wait <dag_id> --timeout 300 Diagnose failures and inspect logs: af runs diagnose <dag_id> <dag_run_id> Inspect task logs: af tasks logs <dag_id> <dag_run_id> <task_id>

Frequently Asked Questions about testing-dags

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to diagnose failures and check task logs in an Airflow pipeline?▼

Diagnosing Airflow pipeline failures involves running a diagnostic command on the specific DAG and run ID, which surfaces root causes. You then fetch individual task logs to inspect detailed execution errors and identify the exact failure point.

Can I trigger an Airflow DAG run and wait for it to complete automatically?▼

Yes, you can trigger an Airflow DAG run and wait for completion automatically by using a trigger command with a specified timeout. This holds the process until the pipeline finishes, returning the final execution state for immediate review.

How do I validate a new Airflow DAG behavior before scheduling it?▼

You validate new Airflow DAG behavior by triggering a manual test run and observing the outcomes. If tasks fail, you review the logs, apply code fixes, and re-trigger the run until the DAG executes without errors.

Why does my Airflow DAG test run keep failing after I apply code fixes?▼

If an Airflow DAG test run keeps failing, you need to fetch the updated task logs to diagnose the new root cause. Iterative testing requires inspecting logs after each fix to ensure the applied changes resolved the specific execution error.

Does this iterative Airflow DAG testing workflow require any external dependencies?▼

No, this iterative Airflow DAG testing workflow does not require external dependencies. It operates by applying guided trigger-debug-fix cycles directly to your existing Airflow environment to validate DAG behavior and remediate issues.

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