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jupyter-live-kernel

Execute stateful Python code through a live Jupyter kernel via the hamelnb script.

6|Updated Jul 12, 2026
Search Tags:#python#data science#repl#kernel#jupyter#notebook#iterative exploration
One-click install
npx skills add https://github.com/josoroma/AppLoop --skill jupyter-live-kernel-josoroma
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-live-kernel
Source: https://github.com/josoroma/AppLoop/tree/main/.hermes/skills/data-science/jupyter-live-kernel
Command: npx skills add https://github.com/josoroma/AppLoop --skill jupyter-live-kernel-josoroma

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jupyterlab, uv.

What problem does it solve? One-shot code execution loses all variables between runs, forcing you to rebuild state repeatedly. This Skill provides a persistent Python REPL backed by a live Jupyter kernel, so variables, imports, and objects survive across executions for iterative exploration. ## Core Features & Use Cases - Stateful Code Execution: Run Python code against a live kernel where variables persist across calls, ideal for incremental data exploration. - Variable Inspection: List and preview live kernel variables to inspect DataFrames, models, and intermediate results without re-running code. - Notebook Cell Editing: Insert, replace, and delete notebook cells programmatically, plus restart-and-run-all verification for reproducibility. - Use Case: While analyzing a dataset, load a CSV into a pandas DataFrame, inspect its shape, try several transformations step by step, and preview intermediate results — all without losing state between steps. ## 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 via the hamelnb script's execute command, which keeps variables, imports, and objects alive across calls. Point it at a notebook path with an active kernel session and each execution builds on the previous state.

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, installs, and process management.

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. You also need the hamelnb repository cloned and a kernel session started through the Jupyter REST API before executing code.

Why does the first Jupyter kernel execution time out?▼

The kernel needs time to initialize after the server starts, so the first execution may hit the 30-second default timeout. Retry the call, or pass a longer timeout such as --timeout 120 for setup or heavy computation.

Can I inspect variables in a running Jupyter kernel?▼

Yes, the variables subcommand lists live kernel variables and previews individual values by name. This lets you inspect DataFrames and intermediate objects without re-executing the code that produced them.

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