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Agent skill

Code Review Excellence

by andrew-yangy in andrew-yangy/gru-ai

Provides comprehensive code review guidance for React 19, Vue 3, Rust, TypeScript, Java, Python, and C/C++.

MITAuto-check: notesDevelopment

Install Code Review Excellence

skills CLI
$ npx skills add andrew-yangy/gru-ai --skill code-review-excellence -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install andrew-yangy/gru-ai code-review-excellence --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/andrew-yangy/gru-ai.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/code-review-excellence .claude/skills/code-review-excellence && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
code-review-excellence
GitHub stars
155
Token cost
~1.7k tokens
SKILL.md length
586 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Provides comprehensive code review guidance for React 19, Vue 3, Rust, TypeScript, Java, Python, and C/C++.

  • Works in 7 steps: The Review Mindset → Effective Feedback → Review Scope → …
  • : reviewing pull requests
  • SKILL.md covers When to Use This Skill, Core Principles, Review Process and Review Techniques, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Code Review Excellence is an agent skill from andrew-yangy/gru-ai. Provides comprehensive code review guidance for React 19, Vue 3, Rust, TypeScript, Java, Python, and C/C++. Helps catch bugs, improve code quality, and give constructive feedback. Use when: reviewing pull requests, conducting PR reviews, code review, reviewing code changes, establishing review standards, mentoring developers, architecture reviews, security audits, checking code quality, finding bugs, giving feedback on code.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Pull requests, Code review and Software architecture. It works with React, Vue.js, C++ and Java. The repository describes itself as: Autonomous AI agent team for one-man companies. Context engineering + harness engineering drive a pipeline that brainstorms, builds, reviews, and ships. The licence is MIT.

When your agent uses it

  • : reviewing pull requests
  • Conducting PR reviews
  • Reviewing code changes
  • Establishing review standards

Example prompts

  • “Use the code-review-excellence skill to provide comprehensive code review guidance for React 19, Vue 3, Rust, TypeScript, Java, Python, and C/C++”
  • “/code-review-excellence”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Grep, Glob, Bash, WebFetch

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. The Review Mindset
  2. Effective Feedback
  3. Review Scope
  4. Context Gathering (2-3 minutes)
  5. High-Level Review (5-10 minutes)
  6. Line-by-Line Review (10-20 minutes)
  7. Summary & Decision (2-3 minutes)

What it can do on your machine

Read from SKILL.md and the folder at commit 8fba479. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Grep
    • Glob
    • Bash
    • WebFetch

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Code Review Excellence loads about 1.7k tokens when it runs. Until then it costs about 113 tokens; SKILL.md has 586 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~113
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Grep, Glob, Bash, WebFetch

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from andrew-yangy/gru-ai at commit 8fba479, republished under its MIT licence (© andrew-yangy). 586 words, ~1,749 tokens.

Download SKILL.mdSave it as .claude/skills/code-review-excellence/SKILL.md (or your agent's skills folder).
name
code-review-excellence
description
Provides comprehensive code review guidance for React 19, Vue 3, Rust, TypeScript, Java, Python, and C/C++. Helps catch bugs, improve code quality, and give constructive feedback. Use when: reviewing pull requests, conducting PR reviews, code review, reviewing code changes, establishing review standards, mentoring developers, architecture reviews, security audits, checking code quality, finding bugs, giving feedback on code.
allowed-tools
Read, Grep, Glob, Bash, WebFetch

Code Review Excellence

Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.

When to Use This Skill

  • Reviewing pull requests and code changes
  • Establishing code review standards for teams
  • Mentoring junior developers through reviews
  • Conducting architecture reviews
  • Creating review checklists and guidelines
  • Improving team collaboration
  • Reducing code review cycle time
  • Maintaining code quality standards

Core Principles

1. The Review Mindset

Goals of Code Review:

  • Catch bugs and edge cases
  • Ensure code maintainability
  • Share knowledge across team
  • Enforce coding standards
  • Improve design and architecture
  • Build team culture

Not the Goals:

  • Show off knowledge
  • Nitpick formatting (use linters)
  • Block progress unnecessarily
  • Rewrite to your preference
2. Effective Feedback

Good Feedback is:

  • Specific and actionable
  • Educational, not judgmental
  • Focused on the code, not the person
  • Balanced (praise good work too)
  • Prioritized (critical vs nice-to-have)
markdown
❌ Bad: "This is wrong."
✅ Good: "This could cause a race condition when multiple users
         access simultaneously. Consider using a mutex here."

❌ Bad: "Why didn't you use X pattern?"
✅ Good: "Have you considered the Repository pattern? It would
         make this easier to test. Here's an example: [link]"

❌ Bad: "Rename this variable."
✅ Good: "[nit] Consider `userCount` instead of `uc` for
         clarity. Not blocking if you prefer to keep it."
3. Review Scope

What to Review:

  • Logic correctness and edge cases
  • Security vulnerabilities
  • Performance implications
  • Test coverage and quality
  • Error handling
  • Documentation and comments
  • API design and naming
  • Architectural fit

What Not to Review Manually:

  • Code formatting (use Prettier, Black, etc.)
  • Import organization
  • Linting violations
  • Simple typos

Review Process

Phase 1: Context Gathering (2-3 minutes)

Before diving into code, understand:

  1. Read PR description and linked issue
  2. Check PR size (>400 lines? Ask to split)
  3. Review CI/CD status (tests passing?)
  4. Understand the business requirement
  5. Note any relevant architectural decisions
Phase 2: High-Level Review (5-10 minutes)
  1. Architecture & Design - Does the solution fit the problem?
  2. Performance Assessment - Are there performance concerns?
    • For performance-critical code, consult Performance Review Guide
    • Check: Algorithm complexity, N+1 queries, memory usage
  3. File Organization - Are new files in the right places?
  4. Testing Strategy - Are there tests covering edge cases?
Phase 3: Line-by-Line Review (10-20 minutes)

For each file, check:

  • Logic & Correctness - Edge cases, off-by-one, null checks, race conditions
  • Security - Input validation, injection risks, XSS, sensitive data
  • Performance - N+1 queries, unnecessary loops, memory leaks
  • Maintainability - Clear names, single responsibility, comments
Show full SKILL.md (248 more words)Show less
Phase 4: Summary & Decision (2-3 minutes)
  1. Summarize key concerns
  2. Highlight what you liked
  3. Make clear decision:
    • ✅ Approve
    • 💬 Comment (minor suggestions)
    • 🔄 Request Changes (must address)
  4. Offer to pair if complex

Review Techniques

Technique 1: The Checklist Method

Use checklists for consistent reviews. See Security Review Guide for comprehensive security checklist.

Technique 2: The Question Approach

Instead of stating problems, ask questions:

markdown
❌ "This will fail if the list is empty."
✅ "What happens if `items` is an empty array?"

❌ "You need error handling here."
✅ "How should this behave if the API call fails?"
Technique 3: Suggest, Don't Command

Use collaborative language:

markdown
❌ "You must change this to use async/await"
✅ "Suggestion: async/await might make this more readable. What do you think?"

❌ "Extract this into a function"
✅ "This logic appears in 3 places. Would it make sense to extract it?"
Technique 4: Differentiate Severity

Use labels to indicate priority:

  • 🔴 [blocking] - Must fix before merge
  • 🟡 [important] - Should fix, discuss if disagree
  • 🟢 [nit] - Nice to have, not blocking
  • 💡 [suggestion] - Alternative approach to consider
  • 📚 [learning] - Educational comment, no action needed
  • 🎉 [praise] - Good work, keep it up!

Language-Specific Guides

根据审查的代码语言，查阅对应的详细指南：

Language/FrameworkReference FileKey Topics
ReactReact GuideHooks, useEffect, React 19 Actions, RSC, Suspense, TanStack Query v5
Vue 3Vue GuideComposition API, 响应性系统, Props/Emits, Watchers, Composables
RustRust Guide所有权/借用, Unsafe 审查, 异步代码, 错误处理
TypeScriptTypeScript Guide类型安全, async/await, 不可变性
PythonPython Guide可变默认参数, 异常处理, 类属性
JavaJava GuideJava 17/21 新特性, Spring Boot 3, 虚拟线程, Stream/Optional
GoGo Guide错误处理, goroutine/channel, context, 接口设计
CC Guide指针/缓冲区, 内存安全, UB, 错误处理
C++C++ GuideRAII, 生命周期, Rule of 0/3/5, 异常安全
CSS/Less/SassCSS Guide变量规范, !important, 性能优化, 响应式, 兼容性
QtQt Guide对象模型, 信号/槽, 内存管理, 线程安全, 性能

Additional Resources

© andrew-yangy, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/code-review-excellence of andrew-yangy/gru-ai.

Open the folder on GitHubat commit 8fba479

Compare with similar skills

Code Review Excellence next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Code Review Excellence compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Code Review Excellence this skillandrew-yangy/gru-ai155—~1.7kAutomated safety check: NotesMIT
Code Review Skillawesome-skills/code-review-skill2.1k—~2.8kAutomated safety check: NotesMIT
Code Review SkillRain-kl/OpenFlare288—~2.3kAutomated safety check: NotesMIT
Code Revieweralirezarezvani/claude-skills28k1 repos~1.6kAutomated safety check: PassMIT
Cross-Language Coding Standardszereight/gitlab-mcp2k1 repos~1.4kAutomated safety check: PassMIT
Developing Softwaretelagod/code-abyss243—~410Automated safety check: PassMIT

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Categories

Questions about Code Review Excellence

What does Code Review Excellence do?

Provides comprehensive code review guidance for React 19, Vue 3, Rust, TypeScript, Java, Python, and C/C++. Code Review Excellence is an agent skill from andrew-yangy/gru-ai. Provides comprehensive code review guidance for React 19, Vue 3, Rust, TypeScript, Java, Python, and C/C++.

When should I use Code Review Excellence?

Code Review Excellence fits situations like: : reviewing pull requests; conducting PR reviews; reviewing code changes; establishing review standards.

How do I install Code Review Excellence in Claude Code?

Run `npx skills add andrew-yangy/gru-ai --skill code-review-excellence -a claude-code`. Or copy the skill folder (.claude/skills/code-review-excellence in andrew-yangy/gru-ai) into .claude/skills/code-review-excellence in your project. Claude Code loads it when a task matches its description.

How do I install Code Review Excellence in Codex?

Run `npx skills add andrew-yangy/gru-ai --skill code-review-excellence -a codex`. Or copy the skill folder (.claude/skills/code-review-excellence in andrew-yangy/gru-ai) into .agents/skills/code-review-excellence in your project. Codex loads it when a task matches its description.

Can I use Code Review Excellence in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add andrew-yangy/gru-ai --skill code-review-excellence -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/code-review-excellence, .gemini/skills/code-review-excellence, .github/skills/code-review-excellence and .opencode/skills/code-review-excellence in your project.

What does Code Review Excellence need to run?

SKILL.md names no scripts, command-line tools or credentials: Code Review Excellence is instructions for the agent only. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Grep, Glob, Bash, WebFetch.

Does Code Review Excellence access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Code Review Excellence safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Code Review Excellence use?

Code Review Excellence is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Code Review Excellence use?

About 1.7k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Code Review Excellence?

Skills that share tags, products or a category with Code Review Excellence: Code Review Skill (awesome-skills/code-review-skill, 2.1k stars), Code Review Skill (Rain-kl/OpenFlare, 288 stars), Code Reviewer (alirezarezvani/claude-skills, 28k stars) and Cross-Language Coding Standards (zereight/gitlab-mcp, 2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Code Review Excellence?

andrew-yangy (a GitHub user) maintains it in andrew-yangy/gru-ai, which has 155 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on March 11, 2026.

Source: andrew-yangy/gru-ai on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.