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jupyter-live-kernel

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

Updated Jul 3, 2026
Search Tags:#python#data science#repl#exploration#kernel#jupyter#notebook
One-click install
npx skills add https://github.com/CHENHUI-X/toolbox --skill jupyter-live-kernel-chenhui-x
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-live-kernel
Source: https://github.com/CHENHUI-X/toolbox/tree/main/custom-skills/data-science/jupyter-live-kernel
Command: npx skills add https://github.com/CHENHUI-X/toolbox --skill jupyter-live-kernel-chenhui-x

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jupyterlab, uv, and includes scripts (resource) components.

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 step. This Skill provides a stateful Python REPL backed by a live Jupyter kernel, so variables, imports, and objects persist across executions for iterative exploration. ## Core Features & Use Cases - Stateful Code Execution: Run Python code against a live kernel where variables, imports, and DataFrames persist across calls. - Variable Inspection: List and preview live kernel variables to inspect 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 dataset, load a CSV into a pandas DataFrame, inspect its shape, try several transformations step by step, and preview intermediate variables — all without losing state between commands. ## Quick Start Start a JupyterLab server and use the jupyter-live-kernel skill to run Python code in a scratch notebook so I can explore my data iteratively.

Frequently Asked Questions about jupyter-live-kernel

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

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

Use a live Jupyter kernel as a stateful REPL: start a JupyterLab server, create a kernel session via the REST API, then send code with the execute command. Variables, imports, and objects survive between calls.

When should I use a Jupyter kernel instead of one-shot code execution?▼

Use a live kernel for iterative exploration, data science, and ML work where you build state incrementally. Use one-shot execution for standalone scripts that need no persisted variables between runs.

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 be created through the Jupyter REST API before code can execute.

Why does the first Jupyter kernel execution time out?▼

The kernel needs time to initialize after the server starts, so the first execution may exceed the 30-second default timeout. Retry the command or pass a larger timeout such as --timeout 120.

Can I install extra Python packages for the Jupyter kernel?▼

The kernel uses JupyterLab's Python environment, so packages must be installed into that environment. Install additional packages into the JupyterLab tool environment before importing them in executed code.

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