authoring-dags

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name: authoring-dags description: 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. 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.

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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.

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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              |
+-----------------------------------------+

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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:

Command	Purpose
af config connections	What external systems are configured
af config variables	What configuration values exist
af config providers	What operator packages are installed
af config version	Version constraints and features
af dags list	Existing DAGs and naming conventions
af config pools	Resource pools for concurrency

Example discovery questions:

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

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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.

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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

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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

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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.

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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)

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CLI Quick Reference

Phase	Command	Purpose
Discover	af config connections	Available connections
Discover	af config variables	Configuration values
Discover	af config providers	Installed operators
Discover	af config version	Version info
Validate	af dags errors	Parse errors (check first!)
Validate	af dags get <dag_id>	Verify DAG config
Validate	af dags warnings	Configuration warnings
Validate	af dags explore <dag_id>	Full DAG inspection

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

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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.

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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