airflow_dag_factory

What problem does it solve?

This Skill eliminates repetitive, error-prone boilerplate when creating and maintaining many Apache Airflow DAGs by letting you define them declaratively in YAML instead of writing large amounts of Python.

Core Features & Use Cases

• Declarative DAG authoring with YAML: Define DAGs, tasks, task groups, dependencies, schedules, retries, and metadata in a consistent config format.
• Production-ready scaling patterns: Generate many similar DAGs from one template, reuse defaults hierarchically, and support large fleets while keeping YAML maintainable with DRY patterns (anchors).
• Advanced Airflow capabilities in config form: Use dynamic task mapping (partial/expand), dataset-aware scheduling (outlets/inlets and datasets), callbacks, TaskFlow-style decorators, environment-variable expansion, and Jinja2 templating.

Quick Start

Use the airflow_dag_factory skill to generate an Airflow DAG that loads YAML-defined tasks and schedules for multiple similar pipelines in a single project.