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cookbook-audit

Audit Anthropic Cookbook notebooks with rubric scoring and automated validation checks.

51.2k|6.1k|Updated Aug 15, 2023
Search Tags:#code quality#automation#jupyter#notebook audit#technical review#detect-secrets#content validation
One-click install
npx skills add https://github.com/anthropics/claude-cookbooks --skill cookbook-audit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cookbook-audit
Source: https://github.com/anthropics/claude-cookbooks/tree/main/.claude/skills/cookbook-audit
Command: npx skills add https://github.com/anthropics/claude-cookbooks --skill cookbook-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nbconvert, detect-secrets, uv, and includes scripts (resource) components.

What problem does it solve?

Manually auditing Jupyter notebooks for compliance with best practices, technical accuracy, and content quality is time-consuming and prone to human error. This skill automates the initial review process, ensuring consistency and flagging critical issues.

Core Features & Use Cases

  • Automated Technical Checks: Runs detect-secrets and other checks to identify hardcoded API keys, deprecated patterns, and verbose outputs.
  • Markdown Conversion for Review: Converts .ipynb notebooks to a clean Markdown format, making manual content review easier and more focused.
  • Structured Audit Report Generation: Provides a template for a comprehensive audit report, including scoring and specific recommendations.
  • Use Case: A content manager needs to review a new batch of 10 Claude Cookbooks. Instead of manually checking each one, they use this skill to get an automated technical audit and a structured markdown version for quick content review, saving hours per notebook.

Quick Start

Audit the notebook located at 'capabilities/classification/classification.ipynb' using the cookbook-audit skill.

Frequently Asked Questions about cookbook-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I audit Jupyter notebooks for security issues and best practices?▼

Notebook auditing automatically scans for hardcoded secrets, deprecated patterns, and quality violations using detect-secrets and validation checks. It produces a structured report with scores and actionable improvement recommendations, ensuring consistency across multiple notebooks without manual review.

Can I detect secrets and API keys in Jupyter notebooks automatically?▼

Yes, the audit runs detect-secrets to identify hardcoded API keys, tokens, and other sensitive data embedded in notebook cells. It flags these issues in the report so you can remove them before sharing or publishing.

What's the best way to review multiple Jupyter notebooks for quality at scale?▼

Use rubric-based auditing to evaluate batches of notebooks consistently. The skill converts notebooks to Markdown for focused content review and generates structured audit reports, reducing review time per notebook from hours to minutes.

How do I convert a Jupyter notebook to Markdown for easier review?▼

The audit skill converts `.ipynb` notebooks to clean Markdown format using nbconvert, making content, code, and outputs readable without opening Jupyter. This supports faster manual inspection and documentation.

What checks does automated notebook validation include?▼

Validation checks include secret detection, deprecated code patterns, verbose outputs, and compliance with technical best practices. Results are scored against a customizable rubric and compiled into a concise audit report.

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