code_review

Identify bugs, security issues, performance bottlenecks, and readability problems in code.

482|32|Updated Apr 3, 2026
One-click install
npx skills add https://github.com/lithos-ai/motus --skill code-review-lithos-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: code_review
Source: https://github.com/lithos-ai/motus/tree/main/examples/skills/skills/code_review
Command: npx skills add https://github.com/lithos-ai/motus --skill code-review-lithos-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps developers automatically review code for correctness, security, performance, and readability, reducing debugging time and improving code quality.

Core Features & Use Cases

  • Correctness checks to identify bugs and edge cases
  • Security and risk assessment, including potential injections
  • Performance and readability suggestions to simplify refactors
  • Companion reference to a formal review checklist for consistent results

Quick Start

Provide your code snippet or repository URL and ask for a structured code review report.

Frequently Asked Questions about code_review

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

FAQPage Schema
How do I automatically check my code for security vulnerabilities and performance issues?

To automatically check code for security vulnerabilities and performance issues, provide your code snippet or repository URL to receive a structured review report identifying bugs, injections, and bottlenecks.

What is static analysis and how does it help with code readability and refactoring?

Static analysis evaluates source code without execution to identify bugs, security risks, and readability problems, providing actionable suggestions that simplify refactoring and improve overall code quality.

Can I use a code review checklist for large refactors and small patches across different programming languages?

Yes, you can review both small patches and large refactors across various programming languages, as the skill applies a formal review checklist to enforce consistent, prioritized, and actionable fixes.

What's the best way to review a pull request for correctness and edge cases?

The best way to review a pull request for correctness and edge cases is to run a structured code review that checks for bugs, security issues, performance bottlenecks, and readability problems.

Does automated linting cover security and performance bottlenecks or just code formatting?

Automated linting in this context goes beyond formatting to report security vulnerabilities, performance bottlenecks, correctness bugs, and readability issues, following a prioritized review checklist.