Generate a StackQL Jupyter notebook with pystackql query cells.
npx skills add https://github.com/stackql/stackql-skills --skill notebook-stackql
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
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.
Describe your target provider and resources, then run the notebook generator to produce a ready-to-edit .ipynb.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
stackqlInsert StackQL query cells into existing Jupyter notebooks.
kubonyQuery and filter Cloud Logging data across GCP projects using gcloud logging read.
SenseTime-FVGGenerate static and interactive charts from tabular data using matplotlib, seaborn, and plotly.
dougdliuScaffold reproducible Jupyter notebooks from standardized templates.
