Agent skill · data analytics · aradotso
apache-airflow-orchestration
Expert knowledge of Apache Airflow for building, scheduling, and monitoring data pipelines and workflows
Why this skill is useful
Provides specific commands and patterns for setting up and managing Apache Airflow workflows that the AI wouldn't reliably generate on its own.
What it needs
Requires apache-airflow, docker installed locally. About 8k tokens when loaded. Last updated 2026-08-05. 5 stars on the source repository.
What this skill does
Apache Airflow Orchestration Skill by ara.so — Data Skills collection. Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. It allows you to define workflows as Directed Acyclic Graphs (DAGs) in Python code, making them maintainable, versionable, testable, and collaborative. Installation Using pip Using Docker (Recommended for Development) Access the web UI at http://localhost:8080 (default credentials: airflow/airflow). Standalone Quick Start Core Concepts DAG (Directed Acyclic Graph) A DAG defines a workflow with tasks and their dependencies. Tasks must not create cycles. Basic DAG Structure TaskFlow API (Recommended for Airflow 2.0+) Modern, cleaner syntax using decorators: Common Operators BashOperator PythonOperator BranchPythonOperator EmailOperator Sensors Sensors wait for a condition to be met before proceeding. FileSensor TimeDeltaSensor Custom Sensor Connections and Hooks Setting Up Connections Connections store credentials and connection details. Via CLI Via Environment Variables Using Hooks HTTP Hook Example XCom (Cross-Communication) XCom allows tasks to exchange small amounts of data. XCom with Multiple Return Values Task Dependencies Linear Dependencies Parallel Dependencies Complex Dependencies Cross-DAG Dependencies Dynamic Task Generation Dynamic Task Mapping (Airflow 2.3+) Configuration airflow.cfg Key configuration options: Environment Variables Variables Store global configuration values. Set Variables Use Variables in DAGs Variables in Templates Templating with Jinja Airflow uses Jinja templating for many fields. Common Template Variables Custom Macros CLI Commands DAG Management Task Management Database Users Connections Variables Working with Providers Providers extend Airflow with additional operators, hooks, and sensors. Install Providers AWS S3 Example `python from airflow.providers.amazon.aws.hooks.s3 import S3Hook from airflow.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/apache-airflow-orchestration