version: "1.0.2" name: authoring-dags description: Workflow and best practices for writing Apache Airflow DAGs. Use when creating a new DAG, write pipeline code, handling questions about DAG patterns and conventions or extending an existing DAG with a follow-up/downstream task. ANY request shaped like 'add a DAG named X', 'write a pipeline', 'add a task that runs after Y', or 'extend the DAG'. For testing and debugging DAGs, see the testing-dags skill. hooks: Stop:

• hooks:
• type: command

command: "echo 'Remember to test your DAG with the testing-dags skill'"

DAG Authoring Skill

This skill guides you through creating and validating Airflow DAGs using best practices and af CLI commands.

For testing and debugging DAGs, see the testing-dags skill which covers the full test -> debug -> fix -> retest workflow.

Running the CLI

These commands assume af is on PATH. Run via astro otto to get it automatically, or install standalone with uv tool install astro-airflow-mcp.

Workflow Overview

+-----------------------------------------+
| 1. DISCOVER                             |
|    Understand codebase & environment    |
+-----------------------------------------+
                 |
+-----------------------------------------+
| 2. PLAN                                 |
|    Propose structure, get approval      |
+-----------------------------------------+
                 |
+-----------------------------------------+
| 3. IMPLEMENT                            |
|    Write DAG following patterns         |
+-----------------------------------------+
                 |
+-----------------------------------------+
| 4. VALIDATE                             |
|    Check import errors, warnings        |
+-----------------------------------------+
                 |
+-----------------------------------------+
| 5. TEST (with user consent)             |
|    Trigger, monitor, check logs         |
+-----------------------------------------+
                 |
+-----------------------------------------+
| 6. ITERATE                              |
|    Fix issues, re-validate              |
+-----------------------------------------+

Phase 1: Discover

Before writing code, understand the context.

Explore the Codebase

Use file tools to find existing patterns:

• Glob for **/dags/**/*.py to find existing DAGs
• Read similar DAGs to understand conventions
• Check requirements.txt for available packages

Query the Airflow Environment

Use af CLI commands to understand what's available:

Example discovery questions:

• "Is there a Snowflake connection?" -> af config connections
• "What Airflow version?" -> af config version
• "Are S3 operators available?" -> af config providers

Phase 2: Plan

Based on discovery, propose:

1. DAG structure - Tasks, dependencies, schedule
2. Operators to use - Based on available providers
3. Connections needed - Existing or to be created
4. Variables needed - Existing or to be created
5. Packages needed - Additions to requirements.txt

Get user approval before implementing.

Phase 3: Implement

Write the DAG following best practices (see below). Key steps:

1. Create DAG file in appropriate location
2. Update requirements.txt if needed
3. Save the file

Phase 4: Validate

Use `af` CLI as a feedback loop to validate your DAG.

Step 1: Check Import Errors

After saving, check for parse errors (Airflow will have already parsed the file):

af dags errors

• If your file appears -> fix and retry
• If no errors -> continue

Common causes: missing imports, syntax errors, missing packages.

Step 2: Verify DAG Exists

af dags get <dag_id>

Check: DAG exists, schedule correct, tags set, paused status.

Step 3: Check Warnings

af dags warnings

Look for deprecation warnings or configuration issues.

Step 4: Explore DAG Structure

af dags explore <dag_id>

Returns in one call: metadata, tasks, dependencies, source code.

On Astro

If you're running on Astro, you can also validate locally before deploying:

• Parse check: Run astro dev parse to catch import errors and DAG-level issues without starting a full Airflow environment
• DAG-only deploy: Once validated, use astro deploy --dags for fast DAG-only deploys that skip the Docker image build — ideal for iterating on DAG code

Phase 5: Test

See the testing-dags skill for comprehensive testing guidance.

Once validation passes, test the DAG using the workflow in the testing-dags skill:

1. Get user consent -- Always ask before triggering
2. Trigger and wait -- af runs trigger-wait <dag_id> --timeout 300
3. Analyze results -- Check success/failure status
4. Debug if needed -- af runs diagnose <dag_id> <run_id> and af tasks logs <dag_id> <run_id> <task_id>

Quick Test (Minimal)

# Ask user first, then:
af runs trigger-wait <dag_id> --timeout 300

For the full test -> debug -> fix -> retest loop, see testing-dags.

Phase 6: Iterate

If issues found:

1. Fix the code
2. Check for import errors: af dags errors
3. Re-validate (Phase 4)
4. Re-test using the testing-dags skill workflow (Phase 5)

CLI Quick Reference

Testing commands -- See the testing-dags skill for af runs trigger-wait, af runs diagnose, af tasks logs, etc.

Best Practices & Anti-Patterns

For code patterns and anti-patterns, see [reference/best-practices.md](reference/best-practices.md).

Read this reference when writing new DAGs or reviewing existing ones. It covers what patterns are correct (including Airflow 3-specific behavior) and what to avoid.

Related Skills

• testing-dags: For testing DAGs, debugging failures, and the test -> fix -> retest loop
• debugging-dags: For troubleshooting failed DAGs
• deploying-airflow: For deploying DAGs to production (Astro or open-source)
• migrating-airflow-2-to-3: For migrating DAGs to Airflow 3