What problem does it solve? One-shot code execution loses all state between runs, forcing you to re-import libraries, reload data, and redefine variables on every step. This Skill provides a persistent Python REPL backed by a live Jupyter kernel so you can explore data and iterate on code incrementally. ## Core Features & Use Cases - Stateful Code Execution: Run Python code against a live kernel where variables, imports, and objects persist across executions. - Variable Inspection: List and preview live kernel variables to inspect DataFrames 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 runs. - Use Case: Load a CSV into a pandas DataFrame, inspect its columns, try several cleaning transformations step by step, and only keep the cells that work—exactly like working in a Jupyter notebook. ## Quick Start Start a JupyterLab server, create a scratch notebook session, then ask the agent to execute Python code in the live kernel and inspect the resulting variables.
