notebook

Generate a StackQL Jupyter notebook with pystackql query cells.

Updated Mar 25, 2026

Search Tags:#automation #jupyter #notebook #notebook-generation #stackql #pystackql #cloud-queries

Ready to run

npx skills add https://github.com/stackql/stackql-skills --skill notebook-stackql

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: notebook
Source: https://github.com/stackql/stackql-skills/tree/main/skills/notebook
Command: npx skills add https://github.com/stackql/stackql-skills --skill notebook-stackql

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creates a reproducible, guided workflow to generate and edit a StackQL notebook that uses the pystackql magic commands to query cloud and SaaS resources, reducing setup time and boilerplate.

Core Features & Use Cases

Generate a complete Jupyter notebook with pystackql magic cells for setup, provider pulls, authentication, queries, and optional visualizations.
Supports both local and server execution modes and outputs a ready-to-run .ipynb file that can be shared across teams.
Enables rapid prototyping, experimentation, and documentation of StackQL-based cloud analyses.

Quick Start

Describe your target provider and resources, then run the notebook generator to produce a ready-to-edit .ipynb.

Frequently Asked Questions about notebook

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

FAQPage Schema

How do I generate a Jupyter notebook for querying cloud resources with StackQL?▼

To generate a Jupyter notebook for querying cloud resources with StackQL, you describe your target provider and resources, and the generator outputs a ready-to-run .ipynb file containing pystackql setup, authentication, and query cells.

What is a pystackql magic command and how is it used in notebooks?▼

A pystackql magic command is a Jupyter notebook extension that allows you to run StackQL queries directly within interactive cells. The generated notebook includes these magic cells for provider setup, authentication, and executing cloud resource queries.

Do I need to install pystackql before running the generated Jupyter notebook?▼

Yes, you must install pystackql before running the generated Jupyter notebook. The notebook workflow requires the pystackql package to execute the magic commands needed for provider pulls, authentication, and querying cloud and SaaS resources.

Can I add data visualization cells to my StackQL notebook workflow?▼

Yes, you can add data visualization cells to your StackQL notebook workflow. The generator supports creating a complete .ipynb file that includes optional visualization cells alongside the standard setup, authentication, and cloud query blocks.

Does the generated StackQL notebook support both local and server execution modes?▼

Yes, the generated StackQL notebook supports both local and server execution modes. This allows the outputted .ipynb file to be run locally or on a server, enabling reproducible cloud analysis workflows that can be easily shared across teams.