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jupyter-notebook

Execute stateful Python code through a live Jupyter kernel with persistent variables.

16|Updated Sep 22, 2026
Search Tags:#python#data science#repl#kernel#jupyter#iterative#notebook
One-click install
npx skills add https://github.com/igniteenow/robo --skill jupyter-notebook-igniteenow
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-notebook
Source: https://github.com/igniteenow/robo/tree/main/optional-skills/data-science/jupyter-notebook
Command: npx skills add https://github.com/igniteenow/robo --skill jupyter-notebook-igniteenow

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jupyterlab, uv.

What problem does it solve? One-shot code execution loses all state between runs, forcing you to re-import libraries, reload data, and redefine variables on every attempt. This Skill provides a persistent Python REPL via a live Jupyter kernel so you can explore data, iterate on code, and inspect intermediate results incrementally. ## Core Features & Use Cases - Stateful Code Execution: Run Python code against a live kernel where variables, imports, and objects persist across executions. - Variable Inspection: List and preview live kernel variables to debug DataFrames, models, and intermediate results without re-running code. - Notebook Cell Editing: View, insert, replace, and delete notebook cells, plus restart-and-run-all verification for clean top-to-bottom execution. - Use Case: While exploring a large CSV dataset, load it into a pandas DataFrame once, then iteratively filter, aggregate, and plot it across multiple executions without reloading the data each time. ## Quick Start Start a Jupyter kernel session and run my Python exploration code step by step, keeping the DataFrame in memory between steps.

Frequently Asked Questions about jupyter-notebook

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

FAQPage Schema
How do I run Python code with persistent state between executions?▼

Use a live Jupyter kernel through the jupyter_live_kernel script's execute command, which keeps variables, imports, and objects alive across calls. This differs from one-shot script execution where all state is lost after each run.

When should I use a Jupyter kernel instead of a one-shot Python script?▼

Use a Jupyter kernel for iterative exploration, data science, and tasks where you build up state incrementally or inspect intermediate results. Use one-shot execution for self-contained scripts that need no persistent variables.

What are the prerequisites for running a live Jupyter kernel?▼

You need uv installed, JupyterLab installed via uv tool install, and a running Jupyter server. A kernel session must also be created through the Jupyter REST API before code can execute.

Why does Jupyter code execution timeout on the first run?▼

The kernel needs time to initialize after the server starts, so the first execution may timeout. Retry the command, and pass a longer timeout such as --timeout 120 for heavy computation or initial setup.

How do I fix websocket timeouts when executing Jupyter cells?▼

Force zmq transport with the --transport zmq flag, which uses jupyter_client directly and bypasses the broken websocket reply channel. The kernel usually executed fine; only the response channel failed.

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