apache-airflow-dag-creator

Scaffold Apache Airflow DAGs with standardized directory layouts and environment-gated scheduling.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/mporenta/airflow --skill apache-airflow-dag-creator
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: apache-airflow-dag-creator
Source: https://github.com/mporenta/airflow/tree/main/.claude/skills/apache-airflow-dag-creator
Command: npx skills add https://github.com/mporenta/airflow --skill apache-airflow-dag-creator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill guides engineers to quickly scaffold Apache Airflow DAGs that conform to the data-airflow repository's verified conventions, ensuring consistent structure, naming, and callback patterns across pipelines.

Core Features & Use Cases

  • Provides a repeatable directory layout for new DAG projects (dags/<pipeline_name>/src/main.py and daily.py) with a business-logic class for data processing and a ready-to-run DAG file.
  • Enforces environment-gated scheduling, standard default_args, and Slack/Snowflake integration guidance to reduce configuration errors in local, staging, and prod environments.
  • Supports quick-start templates to jumpstart new pipelines, enabling rapid collaboration and standardization across teams.

Quick Start

Create a daily DAG skeleton for a new pipeline named <pipeline> following the standard structure and callback patterns.

Frequently Asked Questions about apache-airflow-dag-creator

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

FAQPage Schema
How do I scaffold an Apache Airflow DAG that follows standard directory conventions?

To scaffold an Apache Airflow DAG, this skill automates creating a standardized directory layout like dags/<pipeline>/src/main.py and daily.py, enforcing consistent structure, naming, and callback patterns across pipelines.

How do I configure environment-gated scheduling for Airflow pipelines?

Configuring environment-gated scheduling for Airflow pipelines is handled automatically, applying environment-specific gating patterns across development, staging, and production workflows to prevent accidental execution in the wrong environment.

What are the required default args and callback patterns for a new Airflow pipeline?

Required default args for a new Airflow pipeline include documented defaults for start_date, retries, and tagging, alongside standard Slack and Snowflake integration guidance to ensure consistent callback patterns and reduce configuration errors.

Can I use this Airflow DAG scaffolder for existing data pipeline repositories?

This Airflow DAG scaffolder targets new pipeline work in dags/ and related src structures, applying repository-specific conventions to ensure standardized layouts rather than refactoring pre-existing pipeline files.

Does Airflow DAG scaffolding support Slack and Snowflake integration out of the box?

Airflow DAG scaffolding includes Slack and Snowflake integration guidance out of the box, providing ready-to-run DAG templates with standard callback patterns to reduce configuration errors for local, staging, and prod environments.