code-review-ai-ai-review
使用AI自动化代码审查
将手动代码审查转变为AI辅助的自动化质量保证。该技能将静态分析工具与Claude和GPT模型相结合,在早期发现安全漏洞、性能问题和架构问题。
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前往 设置 → 功能 → 技能 → 上传技能
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测试它
正在使用“code-review-ai-ai-review”。 Review this security-sensitive authentication module
预期结果:
- ## Security Review Findings **CRITICAL - SQL Injection** - File: `src/auth/login.ts:42` - String concatenation with user input enables SQL injection - Fix: Use parameterized queries **HIGH - Weak Password Storage** - File: `src/auth/user.ts:15` - Using MD5 for password hashing - Fix: Use bcrypt or Argon2
正在使用“code-review-ai-ai-review”。 Analyze this database query for performance issues
预期结果:
- ## Performance Analysis **HIGH - N+1 Query Detected** - File: `src/api/users.js:28` - Loop contains 5 database calls - Impact: 100 users = 500 queries - Fix: Use JOIN or batch loading
正在使用“code-review-ai-ai-review”。 Review microservice architecture changes
预期结果:
- ## Architecture Review **WARNING - Shared Database** - Service boundaries violated - Fix: Implement database-per-service pattern **INFO - Missing Circuit Breaker** - External API calls lack resilience - Recommendation: Add circuit breaker pattern
安全审计
安全All 53 static findings are false positives. The skill is a legitimate code review assistant that integrates security scanning tools (SonarQube, CodeQL, Semgrep, TruffleHog) with AI models. External commands, environment access, and network calls are all required for its core function of automated code analysis and GitHub integration.
低风险问题 (6)
检测到的模式
质量评分
你能构建什么
自动化拉取请求审查
与CI/CD管道集成以自动审查每个拉取请求,发布包含安全、性能和架构反馈的结构化评论。
安全重点审计
使用CodeQL和Semgrep运行全面安全分析,识别SQL注入、XSS、身份验证绕过和其他关键漏洞。
性能优化
在生产环境之前检测常见的性能反模式,如N+1查询、缺失数据库索引和无界集合。
试试这些提示
Review this pull request for security vulnerabilities and code quality issues:
PR Description: {pr_description}
Code Diff:
{diff}
Focus on: Security bugs, performance issues, and maintainability concerns.Perform a deep security analysis of this code change. Check for:
1. SQL injection and command injection vulnerabilities
2. Authentication and authorization flaws
3. Insecure cryptographic practices
4. Data exposure risks
Code:
{code_snippet}
Static analysis results:
{static_results}Analyze this code change for architectural concerns:
- Does it follow SOLID principles?
- Are dependencies properly managed?
- Is there proper separation of concerns?
- Any potential scalability issues?
Code:
{code}
System context: {architecture_summary}Conduct a comprehensive code review combining static analysis results with AI analysis:
Diff:
{diff}
SonarQube issues: {sonarqube}
CodeQL alerts: {codeql}
Semgrep findings: {semgrep}
Provide prioritized findings with actionable fix examples.最佳实践
- 在AI分析之前运行静态分析工具(CodeQL、Semgrep)以提供上下文数据
- 使用temperature=0.1-0.2进行一致且确定的安全审查
- 设置质量门禁,阻止包含CRITICAL严重级别问题的PR
避免
- 仅依赖AI而没有静态分析上下文-AI可能遗漏已知漏洞模式
- 设置temperature过高(>0.5)导致不一致或编造的发现
- 忽略误报率-始终手动验证关键发现