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validate-dag

Validate Airflow DAG files for import errors and coding standards.

5|1|Updated Sep 23, 2025
Search Tags:#linting#airflow#static-analysis#ci-cd#data-engineering#unity-catalog#dag-validation
One-click install
npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill validate-dag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: validate-dag
Source: https://github.com/RDEWAI/Redefining-DataEngineering-With-AI/tree/main/chapter-6/developer-plugin/skills/validate-dag
Command: npx skills add https://github.com/RDEWAI/Redefining-DataEngineering-With-AI --skill validate-dag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Deploying broken or non-compliant Airflow DAGs causes pipeline failures, import errors, and unplanned downtime, requiring hours of manual debugging to resolve issues that could have been caught earlier in the development cycle.

Core Features & Use Cases

  • Static DAG Validation: Scans Airflow DAG files for syntax errors, missing imports, and structural issues like orphaned tasks or missing schedule intervals.
  • Project Convention Enforcement: Checks for compliance with team standards, including correct path resolution for Airflow job and config paths, and proper Unity Catalog write patterns for Bronze data runners.
  • Use Case: Data engineering teams can integrate this validation into their CI/CD pipeline to automatically reject DAGs with critical issues before they are deployed to production, preventing failed pipeline runs and data delivery delays.

Quick Start

Use the validate-dag skill to check your Airflow DAG file at path 'dags/my_daily_etl_dag.py' for errors and compliance with project standards.

Frequently Asked Questions about validate-dag

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

FAQPage Schema
How do I validate Airflow DAGs for import errors before deploying?▼

You validate Airflow DAGs by running static analysis on the Python files to check for import errors, syntax issues, and structural problems like orphaned tasks or missing schedule intervals. This catches broken pipelines before code is merged.

What's the best way to enforce Airflow coding standards in a CI/CD pipeline?▼

Enforcing Airflow coding standards in a CI/CD pipeline is done by integrating automated linting that checks DAG files for structural requirements like task dependencies and schedule intervals. This automatically rejects non-compliant DAGs before production deployment.

Can I use static analysis to check Unity Catalog write patterns in Airflow DAGs?▼

Yes, static analysis can check Unity Catalog write patterns in Airflow DAGs. The validation process scans for proper Bronze data runner configurations and correct path resolution for job and config paths to ensure team conventions are met.

Why does my Airflow DAG fail after deployment with path resolution bugs?▼

Airflow DAGs fail after deployment with path resolution bugs because static validation was not performed on the Airflow job and config paths during development. Running automated DAG linting catches these path issues before code is merged to production.

Do I need any external dependencies to run Airflow DAG linting?▼

No external dependencies are required to run Airflow DAG linting. The validation operates as a static analysis script that scans your existing DAG files directly, requiring no additional packages or external tools to check for correctness and compliance.

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