react-code-reviewer
Comprehensive code review guidelines for React 19 TypeScript projects
Skills matching the name, description or GitHub author.
Comprehensive code review guidelines for React 19 TypeScript projects
Code review methodology, severity classification, and quality assessment patterns
Systematic code review guidance covering correctness, maintainability, security, and performance. Activates for PR reviews and code quality checks.
Streamlined code review workflow - gets SHAs and invokes superpowers:code-reviewer without permission prompts
This skill should be used when the user asks about "code quality", "file size limits", "function length", "KISS", "DRY", "YAGNI", "SRP", "best practices", "clean code", "cyclomatic complexity", "code review che...
Use when processing code review feedback. Technical evaluation of feedback, verify before implementing. No performative agreement or gratitude.
Local AI code reviews via CodeRabbit CLI. ONLY use when (1) user explicitly requests "coderabbit"/"cr review", OR (2) code changes are high-risk (security, concurrency, complex logic). Rate-limited to 1 review/...
Systematic code review checklist for quality and security
Load PR comments and feedback for code review. Use when the user wants to see reviewer feedback, check for unresolved comments, understand what changes are requested, or review discussion on a PR.
Code review mode - comprehensive review with security, performance, and maintainability focus
This skill should be used when the user asks to "review code", "perform code review", "check code quality", "review PR", "provide code feedback", or needs guidance on code review best practices and standards in...
Comprehensive code review system for Elixir applications using Phoenix, LiveView, Ash Framework, LiveSvelte, TailwindCSS, and DaisyUI. Performs deep analysis of code quality, architectural patterns, test covera...
Provides comprehensive Python code review with focus on quality, bugs, security, and best practices. Use when reviewing Python code, functions, or modules for quality assessment.
Triage and orchestrate code reviews. Analyzes PR intent, identifies touched surfaces, assesses risk, and routes to specialist skills. Does NOT perform detailed review - delegates to specialists. Supports full p...
Perform thorough code reviews following Google's 5-step methodology. Use when reviewing pull requests, change lists, or any code changes to ensure quality, correctness, and maintainability.
Performs comprehensive code review of a GitHub PR. Use when: user wants to review a PR, check code changes, or analyze pull request.
Performs thorough code review with focus on correctness, security, performance, and maintainability. Provides actionable feedback with specific line references.
Hyper-pedantic Django code review skill emulating Monty's correctness-first, multi-tenant-safe review style.
Code style principle-based review - checks SRP (Single Responsibility Principle), DRY (Don't Repeat Yourself), Simplicity First, YAGNI (You Aren't Gonna Need It), and Type Safety. Also evaluates code structure...
Systematic code review with security, performance, and architecture analysis. Provides actionable fix suggestions and GitHub PR integration. Use when reviewing PRs, validating code changes, or checking code qua...
Roast a repository to identify issues, then translate to constructive feedback. Use when user wants code review, wants to find problems, asks to "roast" their code, or wants honest feedback about their codebase...
AI 驱动的代码审查能力,当用户提到代码审查、code review、审查代码、查看代码质量等关键词时自动触发。使用 9 个并行 Agent 进行全维度分析(Bug/质量/安全/性能)。
Review LLM-generated diff classifications for the diffview eval system. Use when the user invokes this skill or pastes yanked case data from evalreview (containing "# Diff Classification Review" header). Evalua...
Activates when user wants code reviewed for quality, best practices, bugs, or improvements. Triggers on "review this code", "check my implementation", "is this code good", "find bugs", "improve this function",...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.