Debugging Dags
Automatically identifies and suggests fixes for code errors using detailed dependency analysis and debugging techniques.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add necatiarslan-debugging-dags npx -- -y @trustedskills/necatiarslan-debugging-dags
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"necatiarslan-debugging-dags": {
"command": "npx",
"args": [
"-y",
"@trustedskills/necatiarslan-debugging-dags"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill allows you to debug Directed Acyclic Graphs (DAGs) within Apache Airflow. It facilitates stepping through tasks, inspecting variables at runtime, and setting breakpoints directly within your VS Code environment. This enables efficient troubleshooting of complex data pipelines and workflow orchestration.
When to use it
- You're encountering errors in an Airflow DAG and need to understand the flow of execution.
- You want to inspect variable values during task execution to identify unexpected behavior.
- You’re developing new Airflow tasks and require a debugging environment for iterative testing.
- You are troubleshooting performance issues within your Airflow pipelines.
Key capabilities
- Stepping through DAG tasks
- Runtime variable inspection
- Breakpoint setting within VS Code
Example prompts
- "Debug this Airflow DAG and step through the 'extract_data' task."
- "Show me the value of the 'start_date' variable when the 'transform_data' task runs."
- "Set a breakpoint in the 'load_data' task to inspect its inputs."
Tips & gotchas
This skill requires an Apache Airflow environment and VS Code with the appropriate extensions installed. Ensure your DAGs are properly configured for debugging within VS Code before using this skill.
Tags
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