jupyter-live-kernel

A stateful Python REPL maintains variables and imports across executions by using a live Jupyter kernel. This approach keeps objects persistent for iterative development without re-running previous code blocks.

Running a Jupyter kernel from the command line provides notebook-like interactivity for data analysis without needing a browser. You can execute code snippets and inspect results directly in the terminal through a stateful REPL.

Yes, a stateful Python REPL requires a running Jupyter server and a Python environment. The skill orchestrates the live kernel to manage notebook sessions, execute code, and inspect variables through the workflow.

Yes, you can start, manage, and interact with a Jupyter kernel directly without a browser. The skill orchestrates notebook sessions via the command line, allowing you to execute code and inspect variables programmatically.

A stateful REPL exposes a live Jupyter kernel for API exploration, keeping variables persistent across runs. This differs from standard scripts by maintaining state incrementally, so you can test API calls and inspect objects without reloading the environment.

You should avoid a live Jupyter kernel if your task requires isolated, stateless executions or if you lack a running Jupyter server and Python environment. The persistent state is designed for iterative development, not independent script runs.