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AjayRajan05
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jupyter-live-kernel

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

5|2|Updated Mar 12, 2026
Search Tags:#python#data science#repl#kernel#jupyter#notebook#iterative exploration
One-click install
npx skills add https://github.com/AjayRajan05/VoiceOS --skill jupyter-live-kernel-ajayrajan05
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-live-kernel
Source: https://github.com/AjayRajan05/VoiceOS/tree/main/skills/bundled/community/data-science/jupyter-live-kernel
Command: npx skills add https://github.com/AjayRajan05/VoiceOS --skill jupyter-live-kernel-ajayrajan05

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jupyterlab, uv.

What problem does it solve? One-shot code execution loses all variables between runs, making iterative data exploration, API experimentation, and incremental debugging painful. 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, imports, and DataFrames survive 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 new dataset, load a CSV into a pandas DataFrame, inspect its shape and columns, try several cleaning transformations, and preview the results step by step without losing state between attempts. ## Quick Start Start a JupyterLab server, create a scratch notebook session, then ask the assistant to run Python code in the live kernel and inspect the resulting variables.

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 execute code against the notebook. Variables, imports, and objects persist across every execute call.

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

Use a live kernel for iterative exploration, data science, and tasks where state must carry across steps. Use one-shot execution for standalone scripts, and a terminal for shell commands, builds, installs, and process management.

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

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

Why does the first code execution against a Jupyter kernel time out?▼

The kernel needs a moment to initialize after the server starts, so the first execution may hit the default 30-second timeout. Retrying usually succeeds, and you can pass a longer timeout such as --timeout 120 for heavy operations.

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

Use the variables command with the notebook path to list all live variables, or preview a specific variable by name. This lets you check intermediate DataFrames and objects without re-running earlier code.

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