authoring-dags

by astronomer

Guided workflow for creating Apache Airflow DAGs with validation and testing integration. Structured six-phase approach: discover environment and existing patterns, plan DAG structure, implement following best practices, validate with af CLI commands, test with user consent, and iterate on fixes CLI commands for discovery ( af config connections , af config providers , af dags list ) and validation ( af dags errors , af dags get , af dags explore ) provide immediate feedback on DAG... npx skills add https://github.com/astronomer/agents --skill authoring-dags Download ZIPGitHub397

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.

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:

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

Phase 2: Plan

Based on discovery, propose:

• DAG structure - Tasks, dependencies, schedule

• Operators to use - Based on available providers

• Connections needed - Existing or to be created

• Variables needed - Existing or to be created

• Packages needed - Additions to requirements.txt

Get user approval before implementing.

Phase 3: Implement

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

• Create DAG file in appropriate location

• Update requirements.txt if needed

• 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 

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 

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:

• Get user consent -- Always ask before triggering

• Trigger and wait -- af runs trigger-wait <dag_id> --timeout 300

• Analyze results -- Check success/failure status

• 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 --timeout 300

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

Phase 6: Iterate

If issues found:

• Fix the code

• Check for import errors: af dags errors

• Re-validate (Phase 4)

• Re-test using the testing-dags skill workflow (Phase 5)

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.

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