Token导航 LogoToken导航TokenDH.com
研究检索执行命令github未标认证来源可访问clear审计提醒

code-review代码审查

Agent Skill

code-review 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,482

周安装

63

GitHub Stars

57,382

下载量

519
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:code-review(代码审查)
来源仓库:https://github.com/shareai-lab/learn-claude-code
仓库路径:skills/code-review
安装命令:
npx skills add https://github.com/shareai-lab/learn-claude-code --skill code-review
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/shareai-lab/learn-claude-code --skill code-review

简介

code-review 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。
  • 通过 npx skills add 命令安装,需结合原始 README 核验具体用法。
  • 安装前建议确认权限范围、维护状态及是否触发联网或文件读写操作。
  • 当前无额外底部简介内容,可参考来源仓库进一步了解功能细节。

SKILL.md

Code Review Skill

You now have expertise in conducting comprehensive code reviews. Follow this structured approach:

Review Checklist

1. Security (Critical)

Check for:

  • Injection vulnerabilities: SQL, command, XSS, template injection
  • Authentication issues: Hardcoded credentials, weak auth
  • Authorization flaws: Missing access controls, IDOR
  • Data exposure: Sensitive data in logs, error messages
  • Cryptography: Weak algorithms, improper key management
  • Dependencies: Known vulnerabilities (check with npm audit, pip-audit)
# Quick security scans
npm audit                    # Node.js
pip-audit                    # Python
cargo audit                  # Rust
grep -r "password\|secret\|api_key" --include="*.py" --include="*.js"

2. Correctness

Check for:

  • Logic errors: Off-by-one, null handling, edge cases
  • Race conditions: Concurrent access without synchronization
  • Resource leaks: Unclosed files, connections, memory
  • Error handling: Swallowed exceptions, missing error paths
  • Type safety: Implicit conversions, any types

3. Performance

Check for:

  • N+1 queries: Database calls in loops
  • Memory issues: Large allocations, retained references
  • Blocking operations: Sync I/O in async code
  • Inefficient algorithms: O(n^2) when O(n) possible
  • Missing caching: Repeated expensive computations

4. Maintainability

Check for:

  • Naming: Clear, consistent, descriptive
  • Complexity: Functions > 50 lines, deep nesting > 3 levels
  • Duplication: Copy-pasted code blocks
  • Dead code: Unused imports, unreachable branches
  • Comments: Outdated, redundant, or missing where needed

5. Testing

Check for:

  • Coverage: Critical paths tested
  • Edge cases: Null, empty, boundary values
  • Mocking: External dependencies isolated
  • Assertions: Meaningful, specific checks

Review Output Format

## Code Review: [file/component name]

### Summary
[1-2 sentence overview]

### Critical Issues
1. **[Issue]** (line X): [Description]
   - Impact: [What could go wrong]
   - Fix: [Suggested solution]

### Improvements
1. **[Suggestion]** (line X): [Description]

### Positive Notes
- [What was done well]

### Verdict
[ ] Ready to merge
[ ] Needs minor changes
[ ] Needs major revision

Common Patterns to Flag

Python

# Bad: SQL injection
cursor.execute(f"SELECT * FROM users WHERE id = {user_id}")
# Good:
cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))

# Bad: Command injection
os.system(f"ls {user_input}")
# Good:
subprocess.run(["ls", user_input], check=True)

# Bad: Mutable default argument
def append(item, lst=[]):  # Bug: shared mutable default
# Good:
def append(item, lst=None):
    lst = lst or []

JavaScript/TypeScript

// Bad: Prototype pollution
Object.assign(target, userInput)
// Good:
Object.assign(target, sanitize(userInput))

// Bad: eval usage
eval(userCode)
// Good: Never use eval with user input

// Bad: Callback hell
getData(x => process(x, y => save(y, z => done(z))))
// Good:
const data = await getData();
const processed = await process(data);
await save(processed);

Review Commands

# Show recent changes
git diff HEAD~5 --stat
git log --oneline -10

# Find potential issues
grep -rn "TODO\|FIXME\|HACK\|XXX" .
grep -rn "password\|secret\|token" . --include="*.py"

# Check complexity (Python)
pip install radon && radon cc . -a

# Check dependencies
npm outdated  # Node
pip list --outdated  # Python

Review Workflow

  1. Understand context: Read PR description, linked issues
  2. Run the code: Build, test, run locally if possible
  3. Read top-down: Start with main entry points
  4. Check tests: Are changes tested? Do tests pass?
  5. Security scan: Run automated tools
  6. Manual review: Use checklist above
  7. Write feedback: Be specific, suggest fixes, be kind

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

26.92%
按下载量换算140

Antigravity

24.06%
按下载量换算125

Claude Code

16.24%
按下载量换算84

OpenCode

13.79%
按下载量换算72

Cursor

6.89%
按下载量换算36

Gemini CLI

3.77%
按下载量换算20

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/shareai-lab/learn-claude-code --skill code-review;npx skills add shareai-lab/learn-claude-code --skill "code-review" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

继续浏览同类 Skills