Convert YAML configuration files into executable Apache Airflow DAG definitions.
npx skills add https://github.com/minyeamer/linkmerce --skill dag-factory
Please help me install this Agent Skill. Skill: dag-factory Source: https://github.com/minyeamer/linkmerce/tree/main/.agents/skills/dag-factory Command: npx skills add https://github.com/minyeamer/linkmerce --skill dag-factory
Builds and maintains Apache Airflow DAGs without hand-coding repetitive Python by letting you define workflows declaratively in YAML.
Use the dag-factory skill to generate an Airflow DAG from your YAML by installing dag-factory, adding load_yaml_dags in your Airflow loader file, and then creating a DAG YAML under your configured dags folder.
High-intent search queries and answers about installing and using this skill.
To generate Apache Airflow DAGs from YAML, use dag-factory to convert declarative YAML configuration files into executable DAG definitions, eliminating manual Python coding. Install dag-factory, add load_yaml_dags to your Airflow loader, and place DAG YAML files in your dags folder.
Declarative DAG generation defines Airflow workflows in YAML instead of hand-coding Python, needed when maintaining many similar workflows. It provides a consistent low-code structure, reusable defaults, and configuration layering to reduce repetitive DAG authoring overhead.
Yes, YAML-defined DAGs support dynamic task mapping, dataset-based scheduling, custom typed objects using __type__, and callback configuration. These production-ready patterns are built into dag-factory v1.0+ for declarative workflow automation.
dag-factory requires Apache Airflow 2.4+ and Python 3.10+ compatibility. It relies on dag-factory v1.0+ which includes validation and migration support to ensure your YAML configurations generate valid DAG definitions.
The best way to manage shared Airflow DAG configuration is using dag-factory's top-level default block or merged defaults.yml files. This configuration layering lets you define common DAG settings once and reuse them across many similar declarative workflows.
Hand-coding repetitive Python for Airflow DAG authoring creates maintenance overhead and inconsistency. Declarative YAML generation with dag-factory eliminates this by providing a structured, low-code format with validation support, ensuring consistent DAG definitions across workflows.
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