code-review-checklist
Code review guidelines covering code quality, security, and best practices.
- License 许可
- MIT
- 最近更新
- 2026-05-22
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- code-review-checklist
- description
- Code review guidelines covering code quality, security, and best practices.
- allowed-tools
- Read, Glob, Grep
02 资源文件 13
- 参考文档architecture-decision-template.md337 B
- 资产文件data/domain-complexity.csv2.1 KB
- 资产文件data/project-types.csv758 B
- 参考文档steps/step-01-init.md6.4 KB
- 参考文档steps/step-01b-continue.md6.1 KB
- 参考文档steps/step-02-context.md8.4 KB
- 参考文档steps/step-03-starter.md11.7 KB
- 参考文档steps/step-04-decisions.md10.0 KB
- 参考文档steps/step-05-patterns.md10.7 KB
- 参考文档steps/step-06-structure.md10.9 KB
- 参考文档steps/step-07-validation.md11.4 KB
- 参考文档steps/step-08-complete.md3.1 KB
- 参考文档workflow.md1.5 KB
03 SKILL.md 正文
Code Review Checklist
Quick Review Checklist
Correctness
- [ ] Code does what it's supposed to do
- [ ] Edge cases handled
- [ ] Error handling in place
- [ ] No obvious bugs
Security
- [ ] Input validated and sanitized
- [ ] No SQL/NoSQL injection vulnerabilities
- [ ] No XSS or CSRF vulnerabilities
- [ ] No hardcoded secrets or sensitive credentials
- [ ] AI-Specific: Protection against Prompt Injection (if applicable)
- [ ] AI-Specific: Outputs are sanitized before being used in critical sinks
Performance
- [ ] No N+1 queries
- [ ] No unnecessary loops
- [ ] Appropriate caching
- [ ] Bundle size impact considered
Code Quality
- [ ] Clear naming
- [ ] DRY - no duplicate code
- [ ] SOLID principles followed
- [ ] Appropriate abstraction level
Testing
- [ ] Unit tests for new code
- [ ] Edge cases tested
- [ ] Tests readable and maintainable
Documentation
- [ ] Complex logic commented
- [ ] Public APIs documented
- [ ] README updated if needed
AI & LLM Review Patterns (2025)
Logic & Hallucinations
- [ ] Chain of Thought: Does the logic follow a verifiable path?
- [ ] Edge Cases: Did the AI account for empty states, timeouts, and partial failures?
- [ ] External State: Is the code making safe assumptions about file systems or networks?
Prompt Engineering Review
// ❌ Vague prompt in code
const response = await ai.generate(userInput);
// ✅ Structured & Safe prompt
const response = await ai.generate({
system: "You are a specialized parser...",
input: sanitize(userInput),
schema: ResponseSchema
});
Anti-Patterns to Flag
// ❌ Magic numbers
if (status === 3) { ... }
// ✅ Named constants
if (status === Status.ACTIVE) { ... }
// ❌ Deep nesting
if (a) { if (b) { if (c) { ... } } }
// ✅ Early returns
if (!a) return;
if (!b) return;
if (!c) return;
// do work
// ❌ Long functions (100+ lines)
// ✅ Small, focused functions
// ❌ any type
const data: any = ...
// ✅ Proper types
const data: UserData = ...
Review Comments Guide
// Blocking issues use ?
? BLOCKING: SQL injection vulnerability here
// Important suggestions use ?
? SUGGESTION: Consider using useMemo for performance
// Minor nits use ?
? NIT: Prefer const over let for immutable variable
// Questions use ❓
❓ QUESTION: What happens if user is null here?
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