jupyter-live-kernel

To maintain persistent variables in Python across executions, use a live Jupyter kernel for stateful exploration. This allows you to build complex code iteratively while keeping variables and intermediate results accessible across multiple command executions.

Yes, you can use a live Jupyter kernel without requiring new tools. The skill integrates with your existing terminal workflows and utilizes uv, JupyterLab, and a kernel managed by hamelnb for stateful Python data science and ML experimentation.

A stateful REPL is useful for machine learning experimentation because it allows you to iteratively test ideas, inspect intermediate results, and build code step-by-step. The live Jupyter kernel maintains persistent variables across executions for continuous API and data exploration.

Yes, you need uv and JupyterLab to run a live kernel for Python exploration. The stateful REPL requires a running kernel managed by hamelnb, relying on these existing dependencies to maintain persistent variables across executions during iterative development.