Author and validate Airflow DAGs using MCP-based guidance.
npx skills add https://github.com/rory-data/copilot --skill authoring-dags-rory-data
Please help me install this Agent Skill. Skill: authoring-dags Source: https://github.com/rory-data/copilot/tree/main/skills/authoring-dags Command: npx skills add https://github.com/rory-data/copilot --skill authoring-dags-rory-data
Creates and validates Airflow DAGs using standardized patterns and MCP-based guidance to reduce errors and onboarding time for new DAG authors.
Create a new DAG file following the MCP workflow and validate it with the MCP tools before triggering tests.
High-intent search queries and answers about installing and using this skill.
To author Airflow DAGs with standardized patterns, this Skill provides structured templates and MCP-based guidance for creating, validating, and testing DAGs to reduce errors and onboarding time.
The best way to validate Airflow DAGs is through a step-by-step MCP-based workflow that performs import checks, reviews DAG details, and identifies warnings before executing testing workflows.
MCP tooling for Airflow DAG discovery and validation works by enforcing safe, CLI-free interactions to standardize pattern adoption, check imports, and prevent errors across development to production environments.
Yes, you can use MCP tools to debug Airflow DAGs across development to production environments, applying standardized patterns and validation workflows to prevent errors without relying on CLI interactions.
Authoring Airflow DAGs without CLI interactions limits direct command-line execution, relying strictly on MCP tooling for safe discovery, validation, and testing workflows to enforce best-practice standards and prevent errors.
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