dag-factory

Convert YAML configuration files into executable Apache Airflow DAG definitions.

3|Updated Jul 22, 2025

Search Tags:#yaml #configuration management #apache airflow #dynamic task mapping #dataset scheduling #declarative automation #dag generation

Ready to run

npx skills add https://github.com/minyeamer/linkmerce --skill dag-factory

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Builds and maintains Apache Airflow DAGs without hand-coding repetitive Python by letting you define workflows declaratively in YAML.

Core Features & Use Cases

Declarative YAML → Airflow DAGs: Generate DAGs from YAML configuration with a consistent, low-code structure.
Reusable defaults & configuration layering: Share common DAG settings via a top-level default block or merged defaults.yml files.
Production-ready configuration patterns: Support dynamic task mapping, dataset-based scheduling, custom typed objects using type, callbacks, and DAG topology wiring via dependencies.

Quick Start

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.

Frequently Asked Questions about dag-factory

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate Apache Airflow DAGs from YAML files?▼

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.

What is declarative DAG generation and when do I need it for Airflow workflows?▼

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.

Can I use dynamic task mapping and dataset-driven scheduling with YAML-defined DAGs?▼

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.

Does dag-factory work with my Apache Airflow version and Python environment?▼

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.

What's the best way to manage shared configuration across multiple Airflow DAGs?▼

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.

Why should I avoid hand-coding repetitive Python for Airflow DAG authoring?▼

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.