Can I use this for cloud-specific Airflow operators?

Absolutely. It covers a range of operators including S3ToSnowflake, dbt Cloud, and standard SQL execution queries.

What best practices does this skill enforce?

It focuses on task idempotency, keeping tasks small and atomic, using XComs sparingly, and utilizing Jinja templating for dynamic dates.

How does this skill help with Airflow DAGs?

It provides standardized templates and implementation patterns for creating idempotent, scalable, and well-structured Airflow DAGs.

Does it support dynamic task generation?

Yes, the skill includes specific patterns for programmatically creating tasks based on data structures, lists, or external configurations.

Airflow Workflow Architect

Name: Airflow Workflow Architect
Author: timequity

bytimequity

数据科学与机器学习

Designs, optimizes, and implements robust Apache Airflow DAGs using industry-standard operators and scheduling best practices.

关于

This skill transforms Claude into a data engineering specialist for Apache Airflow, offering deep expertise in DAG construction, task orchestration, and pipeline reliability. It provides structured patterns for implementing Python, SQL, and cloud-provider operators while enforcing critical best practices like task idempotency and efficient XCom usage. Whether you are building complex dependency chains or dynamic DAGs that scale with your data, this skill ensures your workflows are maintainable, retry-friendly, and optimized for production environments.

主要功能

Operator selection for Python, SQL, S3, and dbt
0 GitHub stars
Idempotent task design patterns
Advanced dependency mapping (Linear, Parallel, Complex)
Standardized DAG structure templates
Dynamic DAG generation logic

使用场景

Building daily ETL/ELT pipelines for automated data warehousing
Migrating manual data scripts into scheduled, observable Airflow workflows
Implementing dynamic task generation for multi-table processing

关于

主要功能

Operator selection for Python, SQL, S3, and dbt
0 GitHub stars
Idempotent task design patterns
Advanced dependency mapping (Linear, Parallel, Complex)
Standardized DAG structure templates
Dynamic DAG generation logic

使用场景

Building daily ETL/ELT pipelines for automated data warehousing
Migrating manual data scripts into scheduled, observable Airflow workflows
Implementing dynamic task generation for multi-table processing