necatiarslan/airflow-vscode-extension

authoring-dags

Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and conventions. For testing and debugging DAGs, see the testing-dags skill.

First seen Feb 6, 2026

Installation

$ npx skills add necatiarslan/airflow-vscode-extension --skill authoring-dags

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 44
License LICENSE
Default branch main
Open issues 2
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,316 B
  • docs SUMMARY.md 252 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 42 installs

SKILL.md

DAG Authoring Skill

This skill guides you through creating and validating Airflow DAGs using best practices and the VS Code extension tools.

For testing and debugging DAGs, see the testing-dags skill.

Critical Warning: Use Extension Tools

Use the Airflow VS Code extension tools for all Airflow operations. Avoid running Airflow CLI commands for listing DAGs, checking logs, or inspecting runs.

Workflow Overview

  1. Discover
  2. Plan
  3. Implement
  4. Validate
  5. Test (with user consent)
  6. Iterate

Phase 1: Discover

Explore the codebase

Use file tools to find existing patterns:

  • Search for existing DAGs in the repo
  • Read similar DAGs for conventions
  • Check requirements and providers in use

Query Airflow via extension tools

Use these tools to understand the environment:

  • listactivedags and listpauseddags for naming conventions
  • getrunningdags for current activity
  • getdaghistory to see run cadence
  • gotoconnectionsview and gotovariablesview for configuration
  • gotoprovidersview and gotopluginsview for installed components
  • gotoserverhealthview for health checks

Phase 2: Plan

Propose:

  1. DAG structure (tasks, dependencies, schedule)
  2. Operators to use
  3. Connections and variables needed
  4. Package changes if required

Get user approval before implementing.

Phase 3: Implement

  1. Create or update the DAG file
  2. Update dependencies if needed
  3. Save the file

Phase 4: Validate

After the DAG is deployed to Airflow, validate via tools:

  • Confirm the DAG appears in listactivedags or listpauseddags
  • Use getdagsource_code to verify the deployed source
  • Review run history with getdaghistory

Phase 5: Test

Follow the testing-dags skill:

  1. Ask for consent
  2. Trigger with triggerdagrun
  3. Review results with getdagruns and analysedaglatest_run

Notes

  • Avoid CLI checks like airflow dags list or astro dev run for operational status.
  • Use the extension tools for runtime investigation and logs.