Databricks Debug Bundle
Automates Databricks debugging tasks like log collection, environment snapshots, and error reproduction for faster issue resolution.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add databricks-debug-bundle npx -- -y @trustedskills/databricks-debug-bundle
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"databricks-debug-bundle": {
"command": "npx",
"args": [
"-y",
"@trustedskills/databricks-debug-bundle"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill provides debugging capabilities specifically tailored for Databricks environments. It allows AI agents to identify and resolve issues within Spark jobs, notebooks, and associated code. The bundle facilitates pinpointing errors, analyzing execution traces, and suggesting corrective actions to improve performance and reliability.
When to use it
- When troubleshooting failing Databricks notebook cells or Spark jobs.
- To diagnose performance bottlenecks in data processing pipelines running on Databricks.
- For identifying the root cause of unexpected results or errors in complex analytical workflows.
- During development and testing phases to ensure code quality and stability within a Databricks environment.
Key capabilities
- Debugging Spark jobs
- Analyzing execution traces
- Identifying errors in Databricks notebooks
- Suggesting corrective actions
Example prompts
- "Debug this Databricks notebook cell and explain the error."
- "Analyze the execution trace for my failing Spark job and suggest improvements."
- “What’s causing this error in my databricks notebook?”
Tips & gotchas
This skill requires a working Databricks environment. Ensure proper authentication and access permissions are configured within your AI agent to interact with your Databricks workspace.
Tags
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