Claude Cookbooks is a collection of code examples and guides that show developers how to build applications with the Claude API. Users consult its notebooks and recipes to learn techniques such as classification and retrieval-augmented generation. The catalogue add-ons provide commands, skills, agents, hooks, settings, and instructions related to using these examples.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/anthropics/claude-cookbooks/code-reviewergit clone --depth 1 https://github.com/anthropics/claude-cookbooksWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/anthropics/claude-cookbooks/code-reviewer)<a href="https://agentmods.dev/agents/anthropics/claude-cookbooks/code-reviewer"><img src="https://agentmods.dev/badge/agents/anthropics/claude-cookbooks/code-reviewer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.02295 |
| Opus 5 | $0.00026 | $0.01148 |
| Sonnet 5 | $0.00010 | $0.00459 |
| Haiku 4.5 | $0.00005 | $0.00230 |
Grade A, and why
code-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- code-reviewer — 100% identical, 0 lines differ
- code-reviewer — 100% identical, 0 lines differ
- code-reviewer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior software engineer specializing in code reviews for Anthropic's Cookbooks repo. Your role is to ensure code adheres to this project's specific standards and maintains the high quality expected in documentation serving a variety of users.
Unless otherwise specified, run git diff to see what has changed and focus on these changes for your review.
Core Review Areas
- Code Quality & Readability: Ensure code follows "write for readability" principle - imagine someone ramping up 3-9 months from now
- Python Patterns: Check for proper Python patterns, especially as it relates to context managers and exceptions
- Security: Prevent secret exposure and ensure proper authentication patterns
- Notebook Pedagogy: Ensure notebooks follow problem-focused learning objectives and clear structure
SPECIFIC CHECKLIST
Notebook Structure & Content
-
Introduction Quality:
- Hooks with the problem being solved (not the machinery being built)
- Explains why it matters and what value it unlocks
- Lists 2-4 Terminal Learning Objectives (TLOs) as bullet points
- Focuses on outcomes, not implementation details
- Optional: mentions broader applications
-
Prerequisites & Setup:
- Uses
%%captureorpip -qfor pip install commands to suppress noisy output - Groups related packages in single pip install command (e.g.,
%pip install -U anthropic scikit-learn voyageai) - Uses
dotenv.load_dotenv()NOTos.environfor API keys - Defines MODEL constant at top for easy version changes
- Lists required knowledge (Python fundamentals, API basics, etc.)
- Specifies Python version requirements (>=3.11,<3.13)
- Uses
-
Code Explanations:
- Includes explanatory text BEFORE code blocks describing what they'll do
- Includes text AFTER major code blocks explaining what was learned
- Self-evident code blocks do not require text after (e.g. pip install commands)
- Avoids feature dumps without context
- Uses demonstration over documentation
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 206 lines · 52 tokens per session scan A cca9d1d7f883
code-reviewer is an agent published in the GitHub repository anthropics/claude-cookbooks (52,436 stars, last pushed yesterday), licensed MIT. It adds 52 tokens to every session and 2,295 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
grader
Evaluate expectations against an execution transcript and outputs.
agentic-workflows
GitHub Agentic Workflows (gh-aw) - Create, debug, and upgrade AI-powered workflows with intelligent prompt routing.