Does it support the latest Airflow features?

Yes, the skill is designed to support modern Airflow patterns, including the TaskFlow API, setup/teardown tasks, and Airflow 3 migration paths.

How does this differ from the testing-dags skill?

The authoring-dags skill focuses on the creation and structural validation of code, while the testing-dags skill is dedicated to the execution, debugging, and log analysis of running pipelines.

Why does this skill use MCP tools instead of Airflow CLI commands?

MCP tools provide structured JSON output that is more reliable and easier for the AI to parse than CLI text, which can be fragile and produce unstructured errors.

Can this skill help find import errors in my DAGs?

Yes, it includes a dedicated validation phase that uses the list_import_errors MCP tool to catch syntax and dependency issues immediately after saving a file.

What is the authoring-dags skill for Claude Code?

It is a specialized skill that provides domain-specific guidance and tools for writing, validating, and managing Apache Airflow DAGs within the Claude Code environment.

Airflow DAG Authoring

Name: Airflow DAG Authoring
Author: astronomer

byastronomer

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데이터 과학 및 ML

Streamlines the creation, planning, and validation of Apache Airflow DAGs using industry best practices and structured MCP tools.

소개

The Airflow DAG Authoring skill provides a comprehensive framework for developing robust data pipelines within Claude Code. It guides users through a structured six-phase workflow—Discovery, Planning, Implementation, Validation, Testing, and Iteration—ensuring that every DAG is built according to modern standards like the TaskFlow API. By leveraging Model Context Protocol (MCP) tools instead of fragile CLI commands, this skill provides real-time feedback on import errors, environment constraints, and configuration warnings, making it an essential tool for data engineers seeking to build production-grade workflows efficiently.

주요 기능

MCP-driven validation to catch import errors and configuration warnings instantly
Enforcement of best practices for TaskFlow API, idempotency, and credential management
Structured workflow from environment discovery to iterative DAG refinement
Integrated discovery of Airflow connections, variables, and installed providers
77 GitHub stars
Deep structural inspection of DAGs including task dependencies and metadata

사용 사례

Building new ETL/ELT data pipelines with complex task dependencies
Refactoring legacy Airflow scripts into modern TaskFlow API patterns
Ensuring new DAGs are compatible with specific environment providers and versions

소개

주요 기능

MCP-driven validation to catch import errors and configuration warnings instantly
Enforcement of best practices for TaskFlow API, idempotency, and credential management
Structured workflow from environment discovery to iterative DAG refinement
Integrated discovery of Airflow connections, variables, and installed providers
77 GitHub stars
Deep structural inspection of DAGs including task dependencies and metadata

사용 사례

Building new ETL/ELT data pipelines with complex task dependencies
Refactoring legacy Airflow scripts into modern TaskFlow API patterns
Ensuring new DAGs are compatible with specific environment providers and versions