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: Adapts behavior based on tool availability, using an incremental generate-execute-validate loop when a cell execution tool exists, or generating the full notebook for user execution otherwise. - BigQuery Integration via %%bqsql Magics: Enforces BigFrames-based querying with mandatory dataframe export, prohibiting the Python BigQuery client library and pandas.read_gbq. - Notebook Structure & Visualization Rules: Defines a story-like layout with markdown sections, plotting rules, 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 creating a notebook that queries data with %%bqsql, validates each cell's output, plots retention curves, and ends with a grounded summary of findings. ## Quick Start Create a Jupyter notebook that analyzes sales trends from my BigQuery table and visualizes the key findings.
