notebook-generator

To generate a Databricks notebook for testing a PySpark class, this Skill locates the Python implementation, identifies the parent Rocks class, and injects runtime metadata and path overrides to produce a ready-to-run test environment.

Yes, you can test a Rocks extension module in Databricks without the full pipeline by generating an isolated notebook that includes environment setup, class instantiation, metadata injection, and result verification steps.

A generated Databricks PySpark test notebook includes environment setup, optional class extension for testing, class instantiation, metadata injection, execution, and result verification steps.

You do not need to manually configure test data paths, because the notebook generator automatically locates runtime config sections and injects path overrides for test data into the Databricks notebook.

The best way to validate changes to a Rocks class in Databricks is generating a focused notebook, saving it as test_[module]_[class_name].ipynb, and executing it to inspect the outputs directly.

The generated Databricks notebook supports metadata injection for isolated experimentation by locating runtime metadata and config sections and applying them during class instantiation and execution.