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Airflow Dag Patterns

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wshobson·vlatest·data·MIT

Analyzes Airflow DAGs to identify common patterns, potential bottlenecks, and optimization opportunities based on wshobson's expertise.

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Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add wshobson/agents --skill airflow-dag-patterns -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

wshobson

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Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

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    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

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    Makes no network calls.

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    Scanned wshobson/agents at 46891e7e60.

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Commit
46891e7e60
Manifest
plugins/data-engineering/skills/airflow-dag-patterns/SKILL.md
Scanned
2 files, 508 lines
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About this skill

What it does

This skill provides reusable patterns and best practices for creating Apache Airflow DAGs (Directed Acyclic Graphs). It helps streamline workflows by offering structured templates, common operators, and modular design approaches that improve maintainability and scalability of data pipelines.

When to use it

  • You're designing a new Airflow pipeline and want to follow industry-standard patterns.
  • Your team is onboarding new members who need guidance on DAG structure and conventions.
  • You're looking to refactor existing DAGs for better clarity, reusability, or performance.
  • You need to implement complex workflows with dependencies, retries, or dynamic task generation.

Key capabilities

  • Predefined DAG templates for common use cases (e.g., ETL pipelines).
  • Best practices for organizing tasks and operators in a modular way.
  • Examples of using sensors, triggers, and dynamic task generation.
  • Guidance on error handling, logging, and monitoring within Airflow workflows.

Example prompts

  • "Show me an example of a DAG pattern for daily data ingestion."
  • "How can I structure my Airflow DAG to support modular tasks?"
  • "Provide a template for a DAG that uses dynamic task generation based on file input."

Tips & gotchas

  • Ensure your Airflow environment is up-to-date to take full advantage of modern DAG features.
  • Customize patterns to fit specific use cases rather than applying them rigidly, as one-size-fits-all approaches may not work well for complex pipelines.