airflow-dag-gen

Name: airflow-dag-gen
Availability: InStock
Author: ClawData

Generate Airflow DAGs from pipeline specifications using project templates.

24|11|Updated Feb 19, 2026

Search Tags:#automation #template #etl #airflow #dag #data-pipelines #dag-generation

Ready to run

npx skills add https://github.com/clawdata/clawdata --skill airflow-dag-gen

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: airflow-dag-gen
Source: https://github.com/clawdata/clawdata/tree/main/skills/airflow-dag-gen
Command: npx skills add https://github.com/clawdata/clawdata --skill airflow-dag-gen

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generating Airflow DAGs for new data pipelines can be time-consuming and error-prone; this skill provides a template-based approach to automate DAG creation from pipeline specifications.

Core Features & Use Cases

Template-driven DAG generation using pre-defined templates such as dag_basic.py.j2, dag_dbt_run.py.j2, and dag_elt.py.j2.
Guidance on best practices including TaskGroups, retry and timeout configurations, meaningful tags, and secure connections/variables handling.
Write the final DAG to the project's dags/ directory and follow naming conventions like dag_<source>to<destination>.

Quick Start

Render the selected DAG template and save the resulting Python file in the project's dags/ directory.

Frequently Asked Questions about airflow-dag-gen

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

FAQPage Schema

How do I generate Airflow DAGs from templates for new data pipelines?▼

You can generate Airflow DAGs by providing a pipeline specification, which this skill uses to select a project template, validate the configuration, and write the final Python file to the dags/ directory.

What Airflow DAG templates are available for automating ELT pipeline creation?▼

Available Airflow DAG templates include dag_basic.py.j2 for simple tasks, dag_dbt_run.py.j2 for dbt executions, and dag_elt.py.j2 for structured ELT workflows, applying best practices like TaskGroups and retry configurations.

Does this DAG generation approach support best practices like TaskGroups and retry handling?▼

Yes, DAG generation applies best practices including TaskGroups, retry and timeout configurations, meaningful tags, and secure connections and variables handling to ensure robust scheduled tasks and data pipelines.

Can I use this template-based approach to create MVP pipelines for data engineering workflows?▼

Yes, this template-based DAG creation is designed for data engineering workflows requiring ELT pipelines and scheduled tasks, making it suitable for rapidly scaffolding new projects and MVP pipelines.

What naming conventions should I follow when writing Airflow DAG files to the dags directory?▼

When writing Airflow DAG files to the dags directory, you should follow naming conventions like dag_<source>_to_<destination> to maintain clear, consistent pipeline specifications across your data engineering workflows.

Why does manually creating Airflow DAGs for new projects take so much time?▼

Manually creating Airflow DAGs is time-consuming and error-prone because you must configure scheduled tasks, retries, and secure connections from scratch, whereas template-based generation automates these specifications safely.