Install
$ agentstack add skill-kilo-org-kilo-marketplace-authoring-dags ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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
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| 1. DISCOVER |
| Understand codebase & environment |
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| 2. PLAN |
| Propose structure, get approval |
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| 3. IMPLEMENT |
| Write DAG following patterns |
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| 4. VALIDATE |
| Check import errors, warnings |
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| 5. TEST (with user consent) |
| Trigger, monitor, check logs |
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| 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:
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
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 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 --timeout 300 - Analyze results -- Check success/failure status
- Debug if needed --
af runs diagnoseandaf tasks logs
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 | Verify DAG config | | Validate | af dags warnings | Configuration warnings | | Validate | af dags explore | 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](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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Kilo-Org
- Source: Kilo-Org/kilo-marketplace
- License: Apache-2.0
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.