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Chensihakniroth
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jupyter-live-kernel

Executes iterative Python code through a stateful live Jupyter kernel.

Updated Jun 7, 2026
Search Tags:#python#data science#repl#kernel#jupyter#notebook#iterative execution
One-click install
npx skills add https://github.com/Chensihakniroth/ANAKOT-AGENT --skill jupyter-live-kernel-chensihakniroth
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-live-kernel
Source: https://github.com/Chensihakniroth/ANAKOT-AGENT/tree/main/skills/data-science/jupyter-live-kernel
Command: npx skills add https://github.com/Chensihakniroth/ANAKOT-AGENT --skill jupyter-live-kernel-chensihakniroth

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? One-shot code execution loses all variables between runs, forcing you to rebuild state every time. This Skill provides a persistent Python REPL backed by a live Jupyter kernel, so variables, imports, and objects survive across executions for true iterative exploration. ## Core Features & Use Cases - Stateful Code Execution: Run Python code against a live kernel where variables and imports persist across calls, ideal for incremental development. - Variable Inspection: List and preview live kernel variables to inspect 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 new dataset, load it into a pandas DataFrame once, then iteratively filter, plot, and transform 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 all variables alive between each execution.

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 REST API, then send code with the execute command. Variables, imports, and objects survive across all subsequent calls.

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 persist across steps. Use one-shot execution for stateless scripts, and a terminal for shell commands, builds, and package installs.

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 code can execute.

Why does the first Jupyter kernel execution time out?▼

The kernel needs a moment to initialize after the server starts, so the first execution may hit the 30-second default timeout. Simply retry the command, or 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 inspect DataFrames and objects without re-executing the code that created them.

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