Airflow
Airflow orchestrates complex data pipelines by scheduling tasks, ensuring efficient workflow execution and reliable data processing.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add airflow npx -- -y @trustedskills/airflow
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"airflow": {
"command": "npx",
"args": [
"-y",
"@trustedskills/airflow"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The Airflow skill enables AI agents to interact with Apache Airflow, a platform used to programmatically author, schedule, and monitor workflows. It allows users to define complex data pipelines, manage dependencies between tasks, and execute them on a schedule or manually.
When to use it
- Automating ETL processes for data engineering teams
- Orchestrating machine learning model training and deployment workflows
- Scheduling and monitoring batch jobs in cloud environments
Key capabilities
- Define and schedule DAGs (Directed Acyclic Graphs)
- Monitor task execution status and logs
- Integrate with various data sources and tools like databases, APIs, and cloud storage
Example prompts
- "Create a DAG that extracts data from a PostgreSQL database and loads it into S3 every hour."
- "Monitor the status of all active tasks in my Airflow instance and alert me if any fail."
- "Generate a report showing the execution time of each task in the last 7 days."
Tips & gotchas
- Ensure your AI agent has access to an Airflow instance and proper authentication credentials.
- Be mindful of DAG dependencies and scheduling intervals to avoid conflicts or redundant executions.
Tags
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