Does this skill help with notebook security?

Yes, it integrates automated checks using detect-secrets to identify hardcoded API keys or credentials before they are committed to a repository.

Can I use this for non-Anthropic notebooks?

While optimized for the Anthropic Cookbook style, the general principles of code quality, security scanning, and educational structure are applicable to any Python-based technical tutorial.

What standards does the Cookbook Audit skill use?

The skill utilizes a comprehensive rubric based on Anthropic's style guide, focusing on problem-first framing, specific learning objectives, and the Diataxis framework for technical documentation.

Why does the skill convert .ipynb files to markdown?

It generates a markdown version in a temporary folder to provide a cleaner review environment, allowing you to focus on the code and narrative without the noise of raw notebook metadata or bulky outputs.

How are the notebooks scored during an audit?

Notebooks are scored out of 20 points, with 5 points allocated to each of the following: Narrative Quality, Code Quality, Technical Accuracy, and Actionability & Understanding.

Cookbook Audit Pro

Name: Cookbook Audit Pro
Author: Microck

byMicrock

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Aprendizaje y Documentación

Audits and scores Jupyter Notebook tutorials against official Anthropic Cookbook standards and educational best practices.

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Cookbook Audit Pro is a specialized skill designed to ensure technical tutorials and AI cookbooks meet the highest standards of quality, security, and pedagogical effectiveness. It facilitates a comprehensive review process that includes automated secret detection, technical validation, and a structured manual audit based on the Diataxis framework. By evaluating notebooks across four critical dimensions—narrative quality, code standards, technical accuracy, and actionability—it helps developers and technical writers transform raw code examples into professional, agency-building educational resources that align with Anthropic's specific style guidelines.

Características Principales

Automated security scanning for hardcoded API keys and credentials
Automatic notebook-to-markdown conversion for streamlined code review
Generates structured audit reports with prioritized improvement recommendations
Narrative review for Terminal and Enabling Learning Objectives
89 GitHub stars
Standardized 20-point scoring system across four key dimensions

Casos de Uso

Performing pre-commit security audits on Jupyter Notebooks to prevent credential leaks
Reviewing community-contributed AI tutorials for official publication
Ensuring internal engineering notebooks follow standardized documentation patterns

Acerca de

Características Principales

Automated security scanning for hardcoded API keys and credentials
Automatic notebook-to-markdown conversion for streamlined code review
Generates structured audit reports with prioritized improvement recommendations
Narrative review for Terminal and Enabling Learning Objectives
89 GitHub stars
Standardized 20-point scoring system across four key dimensions

Casos de Uso

Performing pre-commit security audits on Jupyter Notebooks to prevent credential leaks
Reviewing community-contributed AI tutorials for official publication
Ensuring internal engineering notebooks follow standardized documentation patterns