What problem does it solve? Data analysis tasks often require multi-step queries, iterative exploration, and visualizations that are hard to manage in one-off scripts. This Skill provides structured rules for building, executing, and validating Jupyter notebooks, especially when querying BigQuery, so analyses are reproducible, readable, and error-free. ## Core Features & Use Cases - Conditional Execution Workflow: Enforces a generate-cell, execute-cell, validate-output loop when an execution tool is available, or full-notebook generation with user-run verification when it is not. - BigQuery Integration Rules: Mandates the use of bigframes %%bqsql magics instead of the Python BigQuery client, with required dataframe export and BigFrames-based manipulation and ML. - Notebook Structure & Visualization Standards: Defines a story-like layout with markdown sections, plotting rules, data cleaning constraints, and a mandatory grounded summary. - Use Case: A user asks to explore user retention trends stored in BigQuery. The Skill guides kernel setup, incremental %%bqsql query cells, BigFrames transformations, charts, and a final summary of findings. ## Quick Start Create a Jupyter notebook that queries my BigQuery table and visualizes the key trends in the data.
