Cookbook Audit

Cookbook Audit is an official skill from Anthropic's public claude-cookbooks repository. Its direct primary source is https://github.com/anthropics/claude-cookbooks/tree/main/.claude/skills/cookbook-audit. According to the provider, the skill reviews a requested Cookbook notebook against a rubric and produces a score with concrete recommendations for improvement. It is therefore a text-based instruction set for a compatible Claude Code workflow, not an independent notebook validator, a certification, or a guarantee that a notebook's conclusions are correct.

Purpose and output

The skill organizes review around four dimensions: narrative quality, code quality, technical accuracy, and actionability and understanding. The resulting report should provide an executive summary with an overall score and key strengths and issues. It then justifies each dimension and gives specific recommendations linked to notebook sections or lines. Examples and suggestions turn general criticism into editorial feedback that an author can act on. The score is a review artifact, not an objective measurement of a notebook's scientific or business value.

Rubric and learning objectives

According to the official instructions, a strong introduction explains the problem being solved, why it matters, and concrete Terminal Learning Objectives and Enabling Learning Objectives. Prerequisites should identify required knowledge, tools, and a reproducible setup. The main content should follow a logical progression, place explanatory text before every code block, and explain what was learned afterward. A conclusion should map back to the learning objectives and suggest applications or next resources. These criteria help authors adopt a learner's perspective rather than merely listing features or libraries.

Technical and editorial review

The source recommends automated checks with validate_notebook.py. This script creates a Markdown representation in a temporary directory so that manual review is not obscured by raw notebook structure and outputs. According to the provider, the process also scans for hardcoded keys and credentials through detect-secrets. Manual assessment considers meaningful variable names, comments that explain why, a central MODEL constant, current model names, reproducible dependencies, useful outputs, and the treatment of installation logs. A script that completes successfully does not replace a human inspection of the material.

Practical workflow

Provide the path to a concrete notebook and read style_guide.md first because it contains the governing templates and good or bad examples. Run automated validation next and inspect the generated Markdown file in full. Score each dimension with traceable evidence, identify missing prerequisites, and distinguish a working demonstration from production-ready code. For model calls, external data, and personal or confidential content, clarify authorization, privacy, provenance, and retention before review. A local skill file does not automatically mean local processing, because a connected Claude client may send inputs to its model provider.

E-E-A-T and boundaries

This entry is grounded in Anthropic's published SKILL.md and its named rubrics, validation tools, and report sections. That establishes provenance and the documented purpose, but it does not replace expert review of a notebook. A language model can miss technical defects, phrase a score too confidently, or treat unreliable notebook output as evidence. Reviewers should execute code, inspect sources and dependencies, remove secrets from test data, and independently validate critical recommendations. The skill assesses presentation and documented technical guidance; it does not prove security or scientific validity and does not assume responsibility for decisions based on an audit report.

Classification and compatibility

Cookbook Audit fits Research and Data Analysis because it examines material systematically, applies criteria, and structures findings. Claude Code compatibility follows from its publication under .claude/skills in Anthropic's official repository. Actual availability depends on installation, client version, policies, model access, and the local files present. Skill Road stores no credentials, tokens, or notebook contents. For a reliable audit, specify the notebook path, intended audience, permitted execution, and human approval boundaries explicitly.