notebook-auditor

Auditing Jupyter notebooks for reproducibility involves verifying runnability, data integrity, and model validity to prevent hidden state from skewing results. This process uses an evidence-based workflow to output a dated audit report scaffold at docs/audits/.

Notebook data leakage occurs when temporal alignment or preprocessing flaws skew forecasting conclusions. It is checked through rigorous evidence-backed audits that validate reported results and data integrity, producing an actionable patch plan to fix issues.

Validating PINN versus non-PINN comparison results requires auditing notebook experiments to ensure metric trustworthiness and model validity. This generates a 13-section specification report including an executive verdict and critical-priority assessment for dissertation workflows.

Yes, the notebook audit process works without external dependencies, relying solely on its internal scripts and references. It enforces an evidence-based workflow to review notebook-based experiments and output a dated markdown audit report scaffold.

Troubleshooting unrunnable Jupyter notebooks involves applying a rigorous audit workflow that checks runnability, data integrity, and model validity. It outputs a dated report scaffold with an executive verdict to identify and resolve hidden state issues preventing execution.