Does it support the Diataxis framework?

Yes, the skill enforces an 'Action + Understanding' philosophy inspired by Diataxis, prioritizing problem-oriented learning over mere feature lists.

What is the purpose of the cookbook-audit skill?

It is designed to systematically review Jupyter notebooks to ensure they meet specific educational and technical standards, focusing on clarity, safety, and actionable learning goals.

Why does the skill convert .ipynb files to markdown?

Converting to markdown allows for a more efficient review by removing bulky execution outputs and focusing specifically on the narrative and code structure, saving context window space.

What are the core grading dimensions?

Notebooks are evaluated on a scale of 1-5 across four areas: Narrative Quality, Code Quality, Technical Accuracy, and Operability (totaling 20 points).

How does it handle security during the audit?

The skill runs a validation script that uses detect-secrets with custom patterns to identify and prevent the submission of hardcoded API keys or credentials.

Cookbook Quality Auditor

Name: Cookbook Quality Auditor
Author: zk-b612

byzk-b612

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学習とドキュメンテーション

Audits and grades Jupyter notebooks against Anthropic's official style guide and quality standards.

概要

The cookbook-audit skill provides a structured framework for evaluating the narrative flow, technical accuracy, and instructional quality of AI-centric notebooks. It automates the review process by running validation scripts, scanning for hardcoded secrets, and converting notebooks into a clean markdown format for focused evaluation. By applying a 20-point rubric, the skill helps developers and technical writers ensure their content follows the Diataxis framework, moving beyond simple 'hacks' to provide transferable knowledge and production-grade implementation patterns.

主な機能

Actionable reporting with specific suggestions and priority-based improvements
Automated notebook validation and security scanning for hardcoded API keys
Multi-dimensional scoring for narrative, code, accuracy, and operability
Markdown-based review workflow that strips noisy output for better context focus
2 GitHub stars
Consistency checks for the Anthropic 'Problem-First' instructional philosophy

ユースケース

Performing quality assurance on community-contributed AI tutorials and cookbooks
Ensuring translated technical content maintains instructional rigor and style consistency
Standardizing internal technical documentation and AI implementation guides

概要

主な機能

Actionable reporting with specific suggestions and priority-based improvements
Automated notebook validation and security scanning for hardcoded API keys
Multi-dimensional scoring for narrative, code, accuracy, and operability
Markdown-based review workflow that strips noisy output for better context focus
2 GitHub stars
Consistency checks for the Anthropic 'Problem-First' instructional philosophy

ユースケース

Performing quality assurance on community-contributed AI tutorials and cookbooks
Ensuring translated technical content maintains instructional rigor and style consistency
Standardizing internal technical documentation and AI implementation guides