DAG Authoring Skill

This skill guides you through creating and validating Airflow DAGs using best practices and

af

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

Running the CLI

Run all

af

uvx --from astro-airflow-mcp af <command>

Throughout this document,

af

uvx --from astro-airflow-mcp af

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

af config connections

af config variables

af config providers

af config version

af dags list

af config pools

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

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

af config connections

af config variables

af config providers

af config version

af dags errors

af dags get <dag_id>

af dags warnings

af dags explore <dag_id>

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.

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