authoring-dags
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/astronomer/agents --skill authoring-dags- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads authoring-dags automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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:
Globfor**/dags/**/*.pyto find existing DAGsReadsimilar DAGs to understand conventions- Check
requirements.txtfor 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.txtif 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 <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 parseto catch import errors and DAG-level issues without starting a full Airflow environment - DAG-only deploy: Once validated, use
astro deploy --dagsfor 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>andaf 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:
- 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
FAQ
What does the authoring-dags skill do?
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.
How do I install authoring-dags?
Run npx skills add https://github.com/astronomer/agents --skill authoring-dags in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/authoring-dags/ (personal) or .claude/skills/authoring-dags/ (project).
Which AI agents can use authoring-dags?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is authoring-dags safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains authoring-dags?
It is published by astronomer in astronomer/agents, and it is an official skill from the maker of the technology it covers. It has 1,231 recorded installs.
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