airflow
Python DAG workflow orchestration using Apache Airflow for data pipelines, ETL processes, and scheduled task automation
Best use case
airflow is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Python DAG workflow orchestration using Apache Airflow for data pipelines, ETL processes, and scheduled task automation
Teams using airflow should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/airflow/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How airflow Compares
| Feature / Agent | airflow | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Python DAG workflow orchestration using Apache Airflow for data pipelines, ETL processes, and scheduled task automation
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Airflow ## When to Use This Skill ### USE when: - Building complex data pipelines with task dependencies - Orchestrating ETL/ELT workflows - Scheduling recurring batch jobs - Managing workflows with retries and error handling - Coordinating tasks across multiple systems - Need visibility into workflow execution history - Requiring audit trails and lineage tracking - Building ML pipeline orchestration ### DON'T USE when: - Real-time streaming data (use Kafka, Flink) - Simple cron jobs (use systemd timers, crontab) - CI/CD pipelines (use GitHub Actions, Jenkins) - Low-latency requirements (Airflow has scheduler overhead) - Simple single-task automation (overkill) - Need visual workflow design for non-developers (use n8n) ## Prerequisites ### Installation Options **Option 1: pip (Development)** ```bash # Create virtual environment python -m venv airflow-env source airflow-env/bin/activate # Set Airflow home export AIRFLOW_HOME=~/airflow # Install Airflow with constraints *See sub-skills for full details.* ### Development Setup ```bash # Install development dependencies pip install apache-airflow[dev,postgres,celery,kubernetes] # Install testing tools pip install pytest pytest-airflow # Install linting pip install ruff ``` ## Version History | Version | Date | Changes | |---------|------|---------| | 1.0.0 | 2026-01-17 | Initial release with comprehensive workflow patterns | ## Resources - [Apache Airflow Documentation](https://airflow.apache.org/docs/) - [Airflow Best Practices](https://airflow.apache.org/docs/apache-airflow/stable/best-practices.html) - [Airflow Helm Chart](https://airflow.apache.org/docs/helm-chart/stable/index.html) - [Astronomer Guides](https://www.astronomer.io/guides/) - [Airflow Providers](https://airflow.apache.org/docs/apache-airflow-providers/) --- *This skill provides production-ready patterns for Apache Airflow workflow orchestration, tested across enterprise data pipelines.* ## Sub-Skills - [1. Basic DAG Structure](1-basic-dag-structure/SKILL.md) - [Integration with AWS Services](integration-with-aws-services/SKILL.md) - [1. DAG Design Principles (+3)](1-dag-design-principles/SKILL.md) - [Common Issues (+1)](common-issues/SKILL.md) ## Sub-Skills - [2. Advanced Operators (+6)](2-advanced-operators/SKILL.md)
Related Skills
airflow-integration-with-aws-services
Sub-skill of airflow: Integration with AWS Services.
airflow-2-advanced-operators
Sub-skill of airflow: 2. Advanced Operators (+6).
airflow-1-dag-design-principles
Sub-skill of airflow: 1. DAG Design Principles (+3).
airflow-1-basic-dag-structure
Sub-skill of airflow: 1. Basic DAG Structure.
test-oversized-skill
A test fixture skill that exceeds 200 lines with multiple H2/H3 sections for split testing.
interactive-report-generator
Generate interactive HTML reports with Plotly visualizations from data analysis results. Supports dashboards, charts, and professional styling.
data-validation-reporter
Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting.
claude-reflection
Self-improvement and learning skill that helps Claude learn from user interactions, corrections, and preferences
agent-os-framework
Generate standardized .agent-os directory structure with product documentation, mission, tech-stack, roadmap, and decision records. Enables AI-native workflows.
OrcaFlex Specialist Skill
```yaml
repo-ecosystem-hygiene
Interpret the daily read-only repo ecosystem hygiene audit and route remediation through approved workflows.
domain-knowledge-sweep
Systematic multi-source research of an engineering domain. Spawns parent issue → 6 research subissues (Standards, Academic, Industry, LinkedIn-marketing, Code-audit, Synthesis) → gap implementation subissues. Replaces LinkedIn-only extraction with defensible comprehensive sourcing.