Debugs and verifies Jupyter, Marimo, and Jupytext notebooks by enforcing rigorous execution and traceback checks.
The Notebook Debugger skill provides a robust framework for diagnosing runtime errors in executed notebooks across various formats including marimo, jupytext, and standard Jupyter. It implements a strict 'Iron Law' verification process that prevents false success claims by mandating execution-to-ipynb, cell-level output inspection, and automated traceback detection using tools like jq. This skill is essential for data scientists and developers who need to ensure their notebook-based workflows are fully functional, sequential, and error-free before final delivery.
Características Principales
010 GitHub stars
02Automated traceback detection using jq-based JSON parsing
03Iron Law verification protocol to prevent premature success claims
04Multi-format support for Marimo, Jupytext, and Papermill execution
05Integrated Read tool rendering for inline error inspection
06Cell-level execution count and sequence validation
Casos de Uso
01Verifying complex data science pipelines before committing code
02Debugging silent failures in scheduled Papermill notebook runs
03Converting and testing Marimo or Jupytext scripts as standard notebooks
