# authoring-dags

> Claude/Codex skill by [@astronomer](https://omgskills.com/library/astronomer/index.md) · 454 stars · Updated 2026-10-14

Workflow and best practices for writing Apache Airflow DAGs. Use when creating a new DAG, write pipeline code, handling questions about DAG patterns and conventions or extending an existing DAG with a follow-up/downstream task. ANY request shaped like 'add a DAG named X', 'write a pipeline', 'add a task that runs after Y', or 'extend the DAG'. For testing and debugging DAGs, see the testing-dags skill.

Tags: agentic-workflow, agents, ai, ai-agents, airflow, apache-airflow, claude, cursor, dag, data-engineering, data-pipelines, dbt, llm, mcp, orchestrator, skills, workflow-automation, workflow-management, workflow-orchestration, workflows

## Install

```sh
git clone https://github.com/astronomer/agents /tmp/agents && ln -s /tmp/agents/skills/authoring-dags ~/.claude/skills/authoring-dags
```

## From README

DAG Authoring Skill This skill guides you through creating and validating Airflow DAGs using best practices and af CLI commands. For testing and debugging DAGs, see the testing-dags skill which covers the full test -\> debug -\> fix -\> retest workflow. --- Running the CLI These commands assume af is on PATH. Run via astro otto to get it automatically, or standalone with uv tool astro-airflow-mcp. --- Workflow Overview --- Phase 1: Discover Before writing code, understand the context.

## Links

- HTML page: https://omgskills.com/library/astronomer/agents/authoring-dags/
- GitHub: https://github.com/astronomer/agents
- Author: [@astronomer](https://omgskills.com/library/astronomer/index.md)

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