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notebook-guidance

Guide Jupyter notebook workflows for BigQuery analysis with %%bqsql and BigFrames.

161|37|Updated Apr 2, 2026
Search Tags:#visualization#data-analysis#bigquery#notebook#bigframes#notebook-guidance#kernel-management
One-click install
npx skills add https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack --skill notebook-guidance
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: notebook-guidance
Source: https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack/tree/main/skills/notebook-guidance
Command: npx skills add https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack --skill notebook-guidance

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill guides users to adopt best practices for using Jupyter notebooks in data analysis with BigQuery, enabling reproducible workflows, cleaner notebooks, and safer execution.

Core Features & Use Cases

  • Notebook execution guidance for cell-by-cell and full-notebook flows depending on tool availability.
  • Kernel and environment management including local vs remote, dependencies.
  • BigQuery integration guidance with %%bqsql and BigFrames, avoiding Python client.
  • Notebook structure and visualization best practices including data cleaning and plotting.

Quick Start

Open a notebook and begin with a title, then follow the incremental generate-execute-validate flow for each cell while enabling the %%bqsql magics for BigQuery queries.

Frequently Asked Questions about notebook-guidance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use BigQuery in Jupyter notebooks for data analysis?▼

To use BigQuery in Jupyter notebooks for data analysis, you should utilize the %%bqsql magics and BigFrames libraries. This approach ensures safe, reproducible workflows while avoiding the standard Python BigQuery client.

What is the best way to structure a BigQuery data exploration notebook?▼

The best way to structure a BigQuery data exploration notebook is to start with a clear title and follow an incremental generate-execute-validate flow cell-by-cell. This ensures cleaner notebooks, proper data cleaning, and effective plotting.

Can I use the Python BigQuery client with notebook-guidance workflows?▼

No, you should not use the Python BigQuery client with notebook-guidance workflows. The skill enforces technical requirements that specifically mandate using %%bqsql magics and BigFrames instead of the Python client for BigQuery integration.

How do I manage Jupyter notebook kernels and dependencies for BigQuery?▼

To manage Jupyter notebook kernels and dependencies for BigQuery, you must configure your environment setup to distinguish between local and remote execution. Proper kernel management is required to maintain safe and reproducible notebook workflows.

Does notebook-guidance support full-notebook generation instead of cell-by-cell execution?▼

Yes, notebook-guidance supports full-notebook generation as well as cell-by-cell dynamic execution workflows. The choice between these flows depends on the availability of your specific tooling and environment setup.

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