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jupyter-live-kernel

Execute Python code in a stateful Jupyter kernel REPL with persistent variables.

Updated Mar 24, 2026
Search Tags:#repl#exploration#kernel#jupyter#iterative#stateful#data-science
One-click install
npx skills add https://github.com/1thirteeng3/greenmoire --skill jupyter-live-kernel-1thirteeng3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jupyter-live-kernel
Source: https://github.com/1thirteeng3/greenmoire/tree/main/integrations/hermes-agent/skills/data-science/jupyter-live-kernel
Command: npx skills add https://github.com/1thirteeng3/greenmoire --skill jupyter-live-kernel-1thirteeng3

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a stateful Python REPL through a live Jupyter kernel, enabling variables to persist across executions and supporting iterative exploration and development in data science workflows.

Core Features & Use Cases

  • Stateful Python REPL with persistent variables across notebook sessions for data science, ML experimentation, API exploration, and step-by-step code development.
  • Direct terminal control via hamelnb to manage a live kernel and execute code in real time.
  • No new tools required beyond the Jupyter kernel environment; ideal for experiments that require building up results gradually.

Quick Start

Start a Jupyter kernel session and begin executing cells to build up state incrementally.

Frequently Asked Questions about jupyter-live-kernel

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

FAQPage Schema
How do I keep Python variables persistent across multiple executions for data science?▼

A stateful Python REPL via a live Jupyter kernel provides variable persistence across executions. This enables iterative data science, ML experimentation, and API exploration by preserving intermediate results between code runs.

What do I need to set up a live Jupyter kernel for iterative coding?▼

Setting up a live Jupyter kernel requires uv, JupyterLab, a running Jupyter server, and the hamelnb runtime. These dependencies manage the live kernel session and enable real-time code execution without additional tools.

Can I use a stateful Python REPL for ML experimentation without restarting the kernel?▼

Yes, a stateful Python REPL supports ML experimentation without kernel restarts by maintaining a live Jupyter session. Variables persist across cell executions, allowing you to build results incrementally for data science workflows.

How does a stateful Jupyter kernel compare to a standard Python REPL for API exploration?▼

A stateful Jupyter kernel differs from a standard Python REPL by preserving variables across executions. This makes it better for API exploration where authentication tokens and intermediate responses must be retained between code runs.

When should I not use a live Jupyter kernel session?▼

Avoid using a live Jupyter kernel session for stateless tasks or isolated script execution where variable persistence is unnecessary. It is specifically designed for iterative workflows requiring intermediate result preservation.

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