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Code Review

Official
anthropics·vlatest·backend·Apache-2.0

Analyzes backend code for security vulnerabilities, performance bottlenecks, and adherence to coding standards with human-like reasoning.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add anthropics/knowledge-work-plugins --skill code-review -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

anthropics

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned anthropics/knowledge-work-plugins at da38ec1ee8.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
da38ec1ee8
Manifest
engineering/skills/code-review/SKILL.md
Scanned
1 file, 113 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The Anthropic Code Review skill enables AI agents to analyze source code for logical errors, security vulnerabilities, and adherence to best practices. It provides actionable feedback on implementation quality without requiring manual line-by-line inspection by humans.

When to use it

  • Before merging pull requests in a team environment to catch bugs early.
  • During the development phase to validate new features against existing architectural standards.
  • When auditing third-party libraries or open-source dependencies for potential risks.
  • To generate detailed comments explaining why specific code patterns are problematic.

Key capabilities

  • Identifies logical flaws and runtime errors in various programming languages.
  • Detects security vulnerabilities such as injection attacks or insecure configurations.
  • Evaluates code against defined style guides and maintainability principles.
  • Offers specific, context-aware suggestions for refactoring and optimization.

Example prompts

"Review this Python function for potential race conditions and suggest fixes." "Analyze the provided JavaScript module for security vulnerabilities before deployment." "Check this backend service implementation against our team's API design standards."

Tips & gotchas

Ensure you provide complete context, including relevant dependencies and architectural constraints, to get accurate reviews. While powerful, always verify critical security findings with a human expert before applying changes to production systems.