Automate ETL pipeline orchestration with Apache Airflow DAGs.
npx skills add https://github.com/timequity/plugins --skill airflow-workflows
Please help me install this Agent Skill. Skill: airflow-workflows Source: https://github.com/timequity/plugins/tree/main/craft-coder/data/airflow-workflows Command: npx skills add https://github.com/timequity/plugins --skill airflow-workflows
This Skill provides guidance for designing robust Airflow DAGs, selecting operators, and scheduling patterns to orchestrate data workflows reliably.
Create a daily_etl DAG using the recommended pattern, and run Airflow locally or in your environment to validate orchestration.
High-intent search queries and answers about installing and using this skill.
Design ETL pipelines by structuring DAGs with clear task dependencies, selecting appropriate operators (PythonOperator, BashOperator, SQLExecuteQueryOperator), and implementing idempotent tasks with templating using {{ ds }}. Apply retry and alert configurations to ensure reliability across daily, batch, and streaming workflows in cloud or on-prem environments.
Orchestrate workflows using Apache Airflow DAGs with modular structure, proper operator coverage, and scheduling patterns that support both cloud and on-premises deployments. Configure task dependencies, sensors, and XCom usage minimally to maintain clarity and reliability across different infrastructure setups.
Automate data pipelines using Airflow's scheduling capabilities combined with DAG design patterns that enforce task dependencies and operator selection. Implement templating with {{ ds }} and idempotent task logic to ensure reliable daily, batch, and streaming workflow execution.
Yes. Airflow supports Snowflake integration through S3ToSnowflakeOperator and dbt orchestration via DbtCloudRunJobOperator, enabling you to build end-to-end ETL pipelines that combine data loading, transformation, and scheduling within a single DAG structure.
Select operators based on task requirements: PythonOperator for Python scripts, BashOperator for shell commands, SQLExecuteQueryOperator for database queries, S3ToSnowflakeOperator for cloud data loading, and DbtCloudRunJobOperator for dbt transformations. Proper operator selection ensures efficient, maintainable DAG design.
Minimizing XCom usage prevents performance degradation and state management complexity in Airflow DAGs. Following best practices for task grouping, dependencies, and data passing keeps workflows efficient and maintainable at scale.
interflownexMigrate Apache Airflow DAGs to Managed Service for Apache Airflow.
astronomerConvert dbt Core projects into Airflow DAGs or TaskGroups using Astronomer Cosmos.
sfc-gh-dflippoOptimize dbt model builds and Snowflake queries with materialization and clustering strategies.
mahg-esAutomate ETL orchestration by defining DAGs and workflows across Airflow, Dagster, or Prefect.
