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-execute-validate cell-by-cell flow when an execution tool is available, or full notebook generation with user-run verification when it is not. - BigQuery Integration via %%bqsql: Mandates BigFrames magics instead of the Python BigQuery client, with required dataframe export and BigFrames-based manipulation and ML. - Notebook Structure & Visualization Rules: Defines a storytelling layout with markdown sections, plotting rules, data cleaning constraints, and a standardized final summary. - Use Case: A user asks to explore user retention trends in a BigQuery table. The Skill guides kernel setup, %%bqsql queries into BigFrames dataframes, step-by-step validated cells, charts for each insight, and a grounded summary of findings. ## Quick Start Create a Jupyter notebook that analyzes trends in my BigQuery table using %%bqsql queries and visualizations, executing and validating each cell step by step.
