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reviewing-code审查代码

Agent Skill

reviewing-code 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

9,548

周安装

222

GitHub Stars

公开资料未说明

下载量

1,978
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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

2

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:reviewing-code(审查代码)
来源仓库:https://github.com/microck/ordinary-claude-skills
仓库路径:skills/reviewing-code
安装命令:
npx skills add microck/ordinary-claude-skills --skill "reviewing-code"
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

AgentSkills.tonpx skills
npx skills add microck/ordinary-claude-skills --skill "reviewing-code"

简介

reviewing-code 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结果。
  • 可结合来源仓库和原始 README 核验具体用法。
  • 安装前建议确认权限范围和维护状态。
  • 不涉及直接修改代码,但需评估是否会触发命令执行或文件读写。

SKILL.md

name
Reviewing Code
description
Systematically evaluate code changes for security, correctness, performance, and spec alignment. Use when reviewing PRs, assessing code quality, or verifying implementation against requirements.

Reviewing Code

Evaluate code changes across security, correctness, spec alignment, performance, and maintainability. Apply sequential or parallel review based on scope.

Quick Start

Sequential (small PRs, <5 files):

  1. Gather context from feature specs and acceptance criteria
  2. Review sequentially through focus areas
  3. Report findings by priority
  4. Recommend approval/revision/rework

Parallel (large PRs, >5 files):

  1. Identify independent review aspects (security, API, UI, data)
  2. Spawn specialist agents for each dimension
  3. Consolidate findings
  4. Report aggregate assessment

Context Gathering

Read documentation:

  • docs/feature-spec/F-##-*.md — Technical design and requirements
  • docs/user-stories/US-###-*.md — Acceptance criteria
  • docs/api-contracts.yaml — Expected API signatures
  • docs/data-plan.md — Event tracking requirements (if applicable)
  • docs/design-spec.md — UI/UX requirements (if applicable)
  • docs/system-design.md — Architecture patterns (if available)
  • docs/plans/<slug>/plan.md — Original implementation plan (if available)

Determine scope:

  • Files changed and features affected (F-## IDs)
  • Stories implemented (US-### IDs)
  • API, database, or schema changes

Quality Dimensions

Security (/25)

  • Input validation and sanitization
  • Authentication/authorization checks
  • Sensitive data handling
  • Injection vulnerabilities (SQL, XSS, etc.)

Correctness (/25)

  • Logic matches acceptance criteria
  • Edge cases handled properly
  • Error handling complete
  • Null/undefined checks present

Spec Alignment (/20)

  • APIs match docs/api-contracts.yaml
  • Data events match docs/data-plan.md
  • UI matches docs/design-spec.md
  • Implementation follows feature spec

Performance (/15)

  • Algorithm efficiency
  • Database query optimization
  • Resource usage (memory, network)

Maintainability (/15)

  • Code clarity and readability
  • Consistent with codebase patterns
  • Appropriate abstraction levels
  • Comments where needed

Total: /100

Finding Priority

🔴 CRITICAL (Must fix before merge)

  • Security vulnerabilities
  • Broken functionality
  • Spec violations (API contract breaks)
  • Data corruption risks

Format:

Location: file.ts:123
Problem: [Description]
Impact: [Risk/consequence]
Fix: [Specific change needed]
Spec reference: [docs/api-contracts.yaml line X]

🟡 IMPORTANT (Should fix)

  • Logic bugs in edge cases
  • Missing error handling
  • Performance issues
  • Missing analytics events
  • Accessibility violations

🟢 NICE-TO-HAVE (Optional)

  • Code style improvements
  • Better abstractions
  • Enhanced documentation

✅ GOOD PRACTICES

Highlight what was done well for learning

Review Strategies

Single-Agent Review

Best for <5 files, single concern:

  1. Review sequentially through focus areas
  2. Concentrate on 1-2 most impacted areas
  3. Generate unified report

Parallel Multi-Agent Review

Best for >5 files, multiple concerns:

  1. Spawn specialized agents:

- Security: senior-engineer for vulnerability assessment - Architecture: Explore for pattern compliance - API Contracts: programmer for endpoint validation - Frontend: programmer for UI/UX and accessibility - Documentation: documentor for comment quality and docs

  1. Each agent reviews specific quality dimension
  2. Consolidate findings into single report

Report Structure

# Code Review: [Feature/PR]

## Summary
**Quality Score:** [X/100]
**Issues:** Critical: [N], Important: [N], Nice-to-have: [N]
**Assessment:** [APPROVE / NEEDS REVISION / MAJOR REWORK]

## Spec Compliance
- [ ] APIs match `docs/api-contracts.yaml`
- [ ] Events match `docs/data-plan.md`
- [ ] UI matches `docs/design-spec.md`
- [ ] Logic satisfies story AC

## Findings

### Critical Issues
[Issues with fix recommendations]

### Important Issues
[Issues that should be addressed]

### Nice-to-Have Suggestions
[Optional improvements]

### Good Practices
[What worked well]

## Recommendations
[Next steps: approval, revision needed, etc.]

Fix Implementation

Offer options:

  1. Fix critical + important issues
  2. Fix only critical (minimum for safety)
  3. Provide detailed explanation for learning
  4. Review only (no changes)

Parallel fixes for large revisions:

  • Spawn agents for independent fix areas
  • Coordinate on shared dependencies
  • Document each fix with location, change, and verification method

Document format:

✅ FIXED: [Issue name]
File: [path:line]
Change: [what changed]
Verification: [how to test]

Documentation Updates

Check if specs need updates:

  • Feature spec "Decisions" or "Deviations" if implementation differs
  • Design spec if UI changed
  • API contracts if endpoints modified (requires approval)
  • Data plan if events changed

Always flag for user approval before modifying specs.

Key Points

  • Read all context documents before starting
  • Focus on most impacted areas first
  • Be thorough with security-sensitive code, API changes, and critical user flows
  • Use scoring framework for comprehensive reviews
  • Parallel review scales to large PRs
  • Flag spec deviations for user decision

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

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

trae

30.99%
按下载量换算613

Antigravity

23.28%
按下载量换算460

windsurf

18.24%
按下载量换算361

Claude Code

11.35%
按下载量换算225

Codex

8.45%
按下载量换算167

Gemini CLI

3.42%
按下载量换算68

安全审计

暂无安全审计结果可展示。

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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