review-code
Review code changes for quality, security, and adherence to project conventions. Use after making code changes or when reviewing a pull request.
Best use case
review-code is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Review code changes for quality, security, and adherence to project conventions. Use after making code changes or when reviewing a pull request.
Teams using review-code should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/review-code/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How review-code Compares
| Feature / Agent | review-code | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Review code changes for quality, security, and adherence to project conventions. Use after making code changes or when reviewing a pull request.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Review Code Review the following code: $ARGUMENTS ## Review Scope 1. Run `git diff` to identify changes (or examine specified files) 2. Review each changed file against the checklist below 3. Report findings organized by severity ## Review Checklist ### Correctness - Logic handles all cases including edge cases - Async operations properly awaited - Error handling is appropriate ### Security - No hardcoded secrets - Input validation at boundaries - No injection vectors (command, XSS, prompt) ### TypeScript Quality - Proper type annotations - No unnecessary `any` types - Interfaces defined for data structures ### Performance - No unnecessary re-renders (Vue components) - Efficient algorithms and data structures - Streaming used for AI responses ### Architecture - Follows project patterns (BaseAgent, StateGraph, tsoa) - Correct package boundaries - No circular dependencies ## Output Provide findings as: - 🔴 **Critical**: Must fix before merge - 🟡 **Warning**: Should fix - 🔵 **Suggestion**: Consider improving
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