Do I need a specific style guide for this skill?

Yes, the skill refers to a local style_guide.md file to provide canonical templates and best-practice examples for its evaluation process.

What does the Cookbook Audit skill check for?

It evaluates notebooks based on narrative quality, code standards, technical accuracy, and actionability, while also scanning for hardcoded secrets and credentials.

What is the scoring rubric used by the Auditor?

It uses a 20-point scale divided into four categories: Narrative Quality, Code Quality, Technical Accuracy, and Actionability/Understanding.

How does it handle security during the audit?

The skill runs automated checks using detect-secrets to catch hardcoded API keys and provides a gitignored markdown output to prevent sensitive data leakage.

Does this skill modify my original notebook files?

No, it generates a temporary markdown version of your notebook for review purposes, leaving your original .ipynb files completely untouched.

Cookbook Auditor

Name: Cookbook Auditor
Author: Microck

byMicrock

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Learning & Documentation

Audits technical Jupyter Notebooks against professional style guides to ensure high-quality, secure, and actionable educational content.

About

The Cookbook Audit skill provides a structured framework for reviewing technical tutorials and AI cookbooks, specifically optimized for Anthropic's documentation standards. It combines automated security scanning for hardcoded secrets with a comprehensive manual review process that evaluates narrative flow, code quality, and technical accuracy. By enforcing the Diataxis framework and specific learning objectives, this skill helps developers transform raw code into professional-grade educational resources that prioritize user understanding and transferable knowledge.

Key Features

Detailed audit report generation with prioritized improvement recommendations
Automated security scanning to detect hardcoded API keys and credentials
Objective scoring system across narrative, code, and technical dimensions
89 GitHub stars
Markdown conversion of notebooks for streamlined, context-aware code reviews
Style guide enforcement for learning objectives (TLOs/ELOs) and setup patterns

Use Cases

Reviewing community-contributed AI tutorials for publication readiness
Standardizing internal technical documentation across engineering teams
Performing security and quality audits on legacy Jupyter Notebook collections

About

Key Features

Detailed audit report generation with prioritized improvement recommendations
Automated security scanning to detect hardcoded API keys and credentials
Objective scoring system across narrative, code, and technical dimensions
89 GitHub stars
Markdown conversion of notebooks for streamlined, context-aware code reviews
Style guide enforcement for learning objectives (TLOs/ELOs) and setup patterns

Use Cases

Reviewing community-contributed AI tutorials for publication readiness
Standardizing internal technical documentation across engineering teams
Performing security and quality audits on legacy Jupyter Notebook collections