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.
Install
With your agent
Install OMGSkills skill "googlecloudplatform/data-agent-kit-plugin:skills/notebook-guidance" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.
Manual command
git clone https://github.com/GoogleCloudPlatform/data-agent-kit-plugin /tmp/data-agent-kit-plugin && ln -s /tmp/data-agent-kit-plugin/skills/notebook-guidance ~/.claude/skills/notebook-guidanceFrom README
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. 🔄 Stateful / Iterative Exploration: You need to run a query, inspect results, and decide the next query based on those results while keeping state in memory. Do NOT use a notebook ONLY if: 📝 Simple Fact/Status: The request only requires a single number (e.g., "how many rows") or a status check (e.g., "when was this table updated"). 🏃♂️ Schema Preview: The request is only about the schema or field types.
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