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jupyter-live-kernel

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

Updated May 4, 2026
Search Tags:#python#data science#repl#kernel#jupyter#notebook#iterative execution
One-click install
npx skills add https://github.com/Junkfooooood/hermes-governance --skill jupyter-live-kernel-junkfooooood
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-live-kernel
Source: https://github.com/Junkfooooood/hermes-governance/tree/main/skills/data-science/jupyter-live-kernel
Command: npx skills add https://github.com/Junkfooooood/hermes-governance --skill jupyter-live-kernel-junkfooooood

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 step. This Skill provides a stateful Python REPL backed by a live Jupyter kernel so variables, imports, and objects persist across executions. ## Core Features & Use Cases - Stateful Code Execution: Run Python code against a live kernel where variables, DataFrames, and imports survive across calls. - Variable Inspection: List and preview live kernel variables to inspect intermediate results during exploration. - Notebook Cell Editing: View, insert, replace, and delete notebook cells, plus restart-and-run-all verification for reproducibility. - Use Case: While exploring a dataset, load a CSV into a pandas DataFrame once, then iteratively filter, plot, and transform it across multiple executions without reloading. ## 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 between executions?▼

Use a live Jupyter kernel as a stateful REPL: start a JupyterLab server, create a kernel session via the Jupyter REST API, then send code with the execute command. Variables, imports, and objects persist across all subsequent executions.

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 up state incrementally. Use one-shot execution for standalone scripts that need no persisted variables between runs.

How do I inspect variables in a running Jupyter kernel?▼

Use the variables command with the list subcommand to see all live variables, or the preview subcommand with a variable name to inspect a specific object's value. Both return structured JSON output.

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 for heavy computation.

Can I install packages for the Jupyter kernel environment?▼

The kernel uses JupyterLab's Python environment, so packages must be installed into that environment before use. Install them into the JupyterLab tool environment first, then they are importable in executed code.

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