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 a notebook execution tool is available, or full-notebook generation with user-run verification when it is not. - BigQuery Integration via %%bqsql Magics: Mandates BigFrames %%bqsql magic cells instead of the Python BigQuery client library, with required dataframe export for downstream Python cells. - Notebook Structure & Visualization Rules: Defines a story-like layout with markdown sections, plotting rules (distinct colors, sizing, PCA for clustering), data cleaning constraints, and a mandatory final summary with Q&A, key findings, and next steps. - Use Case: A user asks to explore user retention trends in a BigQuery table. The Skill guides kernel selection, library checks, incremental %%bqsql query cells, BigFrames-based transformation, charts, and a grounded summary. ## 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.
