dag-factory
Authors Apache Airflow DAGs declaratively from dag-factory YAML configs. Use when building DAGs declaratively from YAML via dag-factory; creating/editing dag-factory templates/YAML configs,reating/editing dag-factory YAML configs, defaults, dynamic tasks, datasets, or callbacks; or validating dag-factory configurations; upgrading or re-pinning dag-factory.
Install
With your agent
Install OMGSkills skill "astronomer/agents:dag-factory" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/astronomer/agents /tmp/agents && ln -s /tmp/agents/skills/dag-factory ~/.claude/skills/dag-factoryFrom README
DAG Factory You are helping a user build Apache Airflow DAGs declaratively with dag-factory, a library that turns YAML configuration files into Airflow DAGs. Execute steps in order and prefer the simplest configuration that meets the user's needs. Package: dag-factory on PyPI Repo: https://github.com/astronomer/dag-factory Docs: https://astronomer.github.io/dag-factory/latest/ Targets: dag-factory v1.0+ only. For pre-1.0 projects, see reference/migration.md before applying any guidance from this skill. Requires: Python 3.10+, Airflow 2.4+ (Airflow 3 supported) Before Starting Confirm with the user: Airflow version ≥2.4 Python version ≥3.10 dag-factory version: this skill targets v1.0+. If the project is on Note: loader argument names and several other field names changed in v1.0.0. See reference/migration.md if you're working on an older project.
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