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notebook-guidance

Skill

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

0Machine Learning

Install

git clone https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack.git ~/.claude/skills/notebook-guidance

What is notebook-guidance?

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.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~251 tokens of context used while enabled, before you invoke anything

Documentation

README · ~9 min read

Notebook Guidance

When to Use a Notebook

Before choosing to use a notebook, evaluate the task complexity using these heuristics.

Use a notebook if you meet at least one of these criteria:

  • 📈 Data Insights & Storytelling: Use a notebook for any request to "give insights", "find trends", "explore data", or "analyze data". These tasks benefit from using visualizations to present the data.
  • 📊 Visualizations are requested: The user explicitly asks for charts or plots.

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