cookbook-audit

To audit a Jupyter notebook against a style guide and rubric, you can use a validation script that converts the notebook to Markdown, checks for hardcoded secrets using detect-secrets, and generates a scored report with specific recommendations for narrative and code quality.

An automated notebook audit checks for critical issues and improvement opportunities by scoring narrative quality, code quality, technical accuracy, and actionability, while also verifying secret-safety through pattern scanning and baseline plugins.

To detect hardcoded secrets in an Anthropic Cookbook notebook, you run a validation script that utilizes detect-secrets with project baselines and plugins, providing a fallback pattern scan to ensure no sensitive information is exposed.

Yes, you can use nbconvert to convert notebooks for code review. The audit process relies on nbconvert to transform the notebook into a review-friendly Markdown format before applying technical checks and rubric scoring.

The best way to improve technical accuracy in a notebook is to audit it against a canonical rubric, which surfaces targeted recommendations and example-backed feedback to enhance structure, clarity, and correctness.

Yes, you need a style guide to review notebook code quality. The audit process requires reading a style guide first to establish the canonical standards used to score the notebook and generate actionable improvement recommendations.