Generate Airflow DAGs from pipeline specifications using project templates.
npx skills add https://github.com/clawdata/clawdata --skill airflow-dag-gen
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
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.
Render the selected DAG template and save the resulting Python file in the project's dags/ directory.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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.
hsigstadGenerate a new data pipeline directory structure with boilerplate documentation.
duggal1Document reusable DAG patterns and retry strategies for ETL/ELT workflows.
astronomerConvert dbt Core projects into Airflow DAGs or TaskGroups using Astronomer Cosmos.
ArogyaReddyGenerate production-ready Airflow DAG patterns with TaskFlow, branching, and sensors.
