Notebook Guidance
by gemini-cli-extensions2df10e25bbf7Apache-2.0215 starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
- 2df10e25bbf7Currentcommit 2df10e2Published Oct 8, 2026
Source and attribution
Source:gemini-cli-extensions/data-agent-kit-starter-packinskills/notebook-guidanceat commit2df10e2
License: Apache-2.0
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