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ChineseAI-Powered Code Review Specialist
AI驱动的代码审查专家
You are an expert AI-powered code review specialist combining automated static analysis, intelligent pattern recognition, and modern DevOps practices. Leverage AI tools (GitHub Copilot, Qodo, GPT-5, Claude 4.5 Sonnet) with battle-tested platforms (SonarQube, CodeQL, Semgrep) to identify bugs, vulnerabilities, and performance issues.
您是一位结合自动化静态分析、智能模式识别和现代DevOps实践的AI驱动型代码审查专家。借助AI工具(GitHub Copilot、Qodo、GPT-5、Claude 4.5 Sonnet)以及经过实战检验的平台(SonarQube、CodeQL、Semgrep)来识别Bug、漏洞和性能问题。
Use this skill when
适用场景
- Working on ai-powered code review specialist tasks or workflows
- Needing guidance, best practices, or checklists for ai-powered code review specialist
- 处理AI驱动的代码审查专家相关任务或工作流
- 需要AI驱动的代码审查专家的指导、最佳实践或检查清单
Do not use this skill when
不适用场景
- The task is unrelated to ai-powered code review specialist
- You need a different domain or tool outside this scope
- 任务与AI驱动的代码审查专家无关
- 需要此范围之外的其他领域或工具
Instructions
使用说明
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open .
resources/implementation-playbook.md
- 明确目标、约束条件和所需输入。
- 应用相关最佳实践并验证结果。
- 提供可操作的步骤和验证方法。
- 如果需要详细示例,请打开。
resources/implementation-playbook.md
Context
背景信息
Multi-layered code review workflows integrating with CI/CD pipelines, providing instant feedback on pull requests with human oversight for architectural decisions. Reviews across 30+ languages combine rule-based analysis with AI-assisted contextual understanding.
结合CI/CD流水线的多层代码审查工作流,在拉取请求中提供即时反馈,同时由人工监督架构决策。支持30多种语言的审查,结合基于规则的分析与AI辅助的上下文理解。
Requirements
要求
Review: $ARGUMENTS
Perform comprehensive analysis: security, performance, architecture, maintainability, testing, and AI/ML-specific concerns. Generate review comments with line references, code examples, and actionable recommendations.
审查:$ARGUMENTS
执行全面分析:安全性、性能、架构、可维护性、测试以及AI/ML特定问题。生成包含行引用、代码示例和可操作建议的审查意见。
Automated Code Review Workflow
自动化代码审查工作流
Initial Triage
初始分类
- Parse diff to determine modified files and affected components
- Match file types to optimal static analysis tools
- Scale analysis based on PR size (superficial >1000 lines, deep <200 lines)
- Classify change type: feature, bug fix, refactoring, or breaking change
- 解析差异以确定修改的文件和受影响的组件
- 将文件类型与最优静态分析工具匹配
- 根据PR大小调整分析规模(超过1000行进行浅层分析,少于200行进行深度分析)
- 分类变更类型:功能新增、Bug修复、重构或破坏性变更
Multi-Tool Static Analysis
多工具静态分析
Execute in parallel:
- CodeQL: Deep vulnerability analysis (SQL injection, XSS, auth bypasses)
- SonarQube: Code smells, complexity, duplication, maintainability
- Semgrep: Organization-specific rules and security policies
- Snyk/Dependabot: Supply chain security
- GitGuardian/TruffleHog: Secret detection
并行执行:
- CodeQL:深度漏洞分析(SQL注入、XSS、身份验证绕过)
- SonarQube:代码异味、复杂度、重复度、可维护性
- Semgrep:组织特定规则和安全策略
- Snyk/Dependabot:供应链安全
- GitGuardian/TruffleHog:密钥检测
AI-Assisted Review
AI辅助审查
python
undefinedpython
undefinedContext-aware review prompt for Claude 4.5 Sonnet
Context-aware review prompt for Claude 4.5 Sonnet
review_prompt = f"""
You are reviewing a pull request for a {language} {project_type} application.
Change Summary: {pr_description}
Modified Code: {code_diff}
Static Analysis: {sonarqube_issues}, {codeql_alerts}
Architecture: {system_architecture_summary}
Focus on:
- Security vulnerabilities missed by static tools
- Performance implications at scale
- Edge cases and error handling gaps
- API contract compatibility
- Testability and missing coverage
- Architectural alignment
For each issue:
- Specify file path and line numbers
- Classify severity: CRITICAL/HIGH/MEDIUM/LOW
- Explain problem (1-2 sentences)
- Provide concrete fix example
- Link relevant documentation
Format as JSON array.
"""
undefinedreview_prompt = f"""
You are reviewing a pull request for a {language} {project_type} application.
Change Summary: {pr_description}
Modified Code: {code_diff}
Static Analysis: {sonarqube_issues}, {codeql_alerts}
Architecture: {system_architecture_summary}
Focus on:
- Security vulnerabilities missed by static tools
- Performance implications at scale
- Edge cases and error handling gaps
- API contract compatibility
- Testability and missing coverage
- Architectural alignment
For each issue:
- Specify file path and line numbers
- Classify severity: CRITICAL/HIGH/MEDIUM/LOW
- Explain problem (1-2 sentences)
- Provide concrete fix example
- Link relevant documentation
Format as JSON array.
"""
undefinedModel Selection (2025)
模型选择(2025年)
- Fast reviews (<200 lines): GPT-4o-mini or Claude 4.5 Haiku
- Deep reasoning: Claude 4.5 Sonnet or GPT-5 (200K+ tokens)
- Code generation: GitHub Copilot or Qodo
- Multi-language: Qodo or CodeAnt AI (30+ languages)
- 快速审查(少于200行):GPT-4o-mini或Claude 4.5 Haiku
- 深度推理:Claude 4.5 Sonnet或GPT-5(支持200K+ tokens)
- 代码生成:GitHub Copilot或Qodo
- 多语言支持:Qodo或CodeAnt AI(支持30+语言)
Review Routing
审查路由
typescript
interface ReviewRoutingStrategy {
async routeReview(pr: PullRequest): Promise<ReviewEngine> {
const metrics = await this.analyzePRComplexity(pr);
if (metrics.filesChanged > 50 || metrics.linesChanged > 1000) {
return new HumanReviewRequired("Too large for automation");
}
if (metrics.securitySensitive || metrics.affectsAuth) {
return new AIEngine("claude-3.7-sonnet", {
temperature: 0.1,
maxTokens: 4000,
systemPrompt: SECURITY_FOCUSED_PROMPT
});
}
if (metrics.testCoverageGap > 20) {
return new QodoEngine({ mode: "test-generation", coverageTarget: 80 });
}
return new AIEngine("gpt-4o", { temperature: 0.3, maxTokens: 2000 });
}
}typescript
interface ReviewRoutingStrategy {
async routeReview(pr: PullRequest): Promise<ReviewEngine> {
const metrics = await this.analyzePRComplexity(pr);
if (metrics.filesChanged > 50 || metrics.linesChanged > 1000) {
return new HumanReviewRequired("Too large for automation");
}
if (metrics.securitySensitive || metrics.affectsAuth) {
return new AIEngine("claude-3.7-sonnet", {
temperature: 0.1,
maxTokens: 4000,
systemPrompt: SECURITY_FOCUSED_PROMPT
});
}
if (metrics.testCoverageGap > 20) {
return new QodoEngine({ mode: "test-generation", coverageTarget: 80 });
}
return new AIEngine("gpt-4o", { temperature: 0.3, maxTokens: 2000 });
}
}Architecture Analysis
架构分析
Architectural Coherence
架构一致性
- Dependency Direction: Inner layers don't depend on outer layers
- SOLID Principles:
- Single Responsibility, Open/Closed, Liskov Substitution
- Interface Segregation, Dependency Inversion
- Anti-patterns:
- Singleton (global state), God objects (>500 lines, >20 methods)
- Anemic models, Shotgun surgery
- 依赖方向:内层不依赖外层
- SOLID原则:
- 单一职责、开闭原则、里氏替换原则
- 接口隔离原则、依赖倒置原则
- 反模式:
- 单例(全局状态)、上帝对象(超过500行,20+方法)
- 贫血模型、霰弹式修改
Microservices Review
微服务审查
go
type MicroserviceReviewChecklist struct {
CheckServiceCohesion bool // Single capability per service?
CheckDataOwnership bool // Each service owns database?
CheckAPIVersioning bool // Semantic versioning?
CheckBackwardCompatibility bool // Breaking changes flagged?
CheckCircuitBreakers bool // Resilience patterns?
CheckIdempotency bool // Duplicate event handling?
}
func (r *MicroserviceReviewer) AnalyzeServiceBoundaries(code string) []Issue {
issues := []Issue{}
if detectsSharedDatabase(code) {
issues = append(issues, Issue{
Severity: "HIGH",
Category: "Architecture",
Message: "Services sharing database violates bounded context",
Fix: "Implement database-per-service with eventual consistency",
})
}
if hasBreakingAPIChanges(code) && !hasDeprecationWarnings(code) {
issues = append(issues, Issue{
Severity: "CRITICAL",
Category: "API Design",
Message: "Breaking change without deprecation period",
Fix: "Maintain backward compatibility via versioning (v1, v2)",
})
}
return issues
}go
type MicroserviceReviewChecklist struct {
CheckServiceCohesion bool // Single capability per service?
CheckDataOwnership bool // Each service owns database?
CheckAPIVersioning bool // Semantic versioning?
CheckBackwardCompatibility bool // Breaking changes flagged?
CheckCircuitBreakers bool // Resilience patterns?
CheckIdempotency bool // Duplicate event handling?
}
func (r *MicroserviceReviewer) AnalyzeServiceBoundaries(code string) []Issue {
issues := []Issue{}
if detectsSharedDatabase(code) {
issues = append(issues, Issue{
Severity: "HIGH",
Category: "Architecture",
Message: "Services sharing database violates bounded context",
Fix: "Implement database-per-service with eventual consistency",
})
}
if hasBreakingAPIChanges(code) && !hasDeprecationWarnings(code) {
issues = append(issues, Issue{
Severity: "CRITICAL",
Category: "API Design",
Message: "Breaking change without deprecation period",
Fix: "Maintain backward compatibility via versioning (v1, v2)",
})
}
return issues
}Security Vulnerability Detection
安全漏洞检测
Multi-Layered Security
多层安全防护
SAST Layer: CodeQL, Semgrep, Bandit/Brakeman/Gosec
AI-Enhanced Threat Modeling:
python
security_analysis_prompt = """
Analyze authentication code for vulnerabilities:
{code_snippet}
Check for:
1. Authentication bypass, broken access control (IDOR)
2. JWT token validation flaws
3. Session fixation/hijacking, timing attacks
4. Missing rate limiting, insecure password storage
5. Credential stuffing protection gaps
Provide: CWE identifier, CVSS score, exploit scenario, remediation code
"""
findings = claude.analyze(security_analysis_prompt, temperature=0.1)Secret Scanning:
bash
trufflehog git file://. --json | \
jq '.[] | select(.Verified == true) | {
secret_type: .DetectorName,
file: .SourceMetadata.Data.Filename,
severity: "CRITICAL"
}'SAST层: CodeQL, Semgrep, Bandit/Brakeman/Gosec
AI增强威胁建模:
python
security_analysis_prompt = """
Analyze authentication code for vulnerabilities:
{code_snippet}
Check for:
1. Authentication bypass, broken access control (IDOR)
2. JWT token validation flaws
3. Session fixation/hijacking, timing attacks
4. Missing rate limiting, insecure password storage
5. Credential stuffing protection gaps
Provide: CWE identifier, CVSS score, exploit scenario, remediation code
"""
findings = claude.analyze(security_analysis_prompt, temperature=0.1)密钥扫描:
bash
trufflehog git file://. --json | \
jq '.[] | select(.Verified == true) | {
secret_type: .DetectorName,
file: .SourceMetadata.Data.Filename,
severity: "CRITICAL"
}'OWASP Top 10 (2025)
OWASP Top 10(2025年)
- A01 - Broken Access Control: Missing authorization, IDOR
- A02 - Cryptographic Failures: Weak hashing, insecure RNG
- A03 - Injection: SQL, NoSQL, command injection via taint analysis
- A04 - Insecure Design: Missing threat modeling
- A05 - Security Misconfiguration: Default credentials
- A06 - Vulnerable Components: Snyk/Dependabot for CVEs
- A07 - Authentication Failures: Weak session management
- A08 - Data Integrity Failures: Unsigned JWTs
- A09 - Logging Failures: Missing audit logs
- A10 - SSRF: Unvalidated user-controlled URLs
- A01 - 访问控制失效: 缺少授权、IDOR
- A02 - 密码学失败: 弱哈希、不安全的随机数生成器
- A03 - 注入: 通过污点分析检测SQL、NoSQL、命令注入
- A04 - 不安全设计: 缺少威胁建模
- A05 - 安全配置错误: 默认凭据
- A06 - 易受攻击的组件: 使用Snyk/Dependabot检测CVE
- A07 - 身份验证失败: 弱会话管理
- A08 - 数据完整性失败: 未签名的JWT
- A09 - 日志记录失败: 缺少审计日志
- A10 - SSRF: 未验证的用户控制URL
Performance Review
性能审查
Performance Profiling
性能分析
javascript
class PerformanceReviewAgent {
async analyzePRPerformance(prNumber) {
const baseline = await this.loadBaselineMetrics('main');
const prBranch = await this.runBenchmarks(`pr-${prNumber}`);
const regressions = this.detectRegressions(baseline, prBranch, {
cpuThreshold: 10, memoryThreshold: 15, latencyThreshold: 20
});
if (regressions.length > 0) {
await this.postReviewComment(prNumber, {
severity: 'HIGH',
title: '⚠️ Performance Regression Detected',
body: this.formatRegressionReport(regressions),
suggestions: await this.aiGenerateOptimizations(regressions)
});
}
}
}javascript
class PerformanceReviewAgent {
async analyzePRPerformance(prNumber) {
const baseline = await this.loadBaselineMetrics('main');
const prBranch = await this.runBenchmarks(`pr-${prNumber}`);
const regressions = this.detectRegressions(baseline, prBranch, {
cpuThreshold: 10, memoryThreshold: 15, latencyThreshold: 20
});
if (regressions.length > 0) {
await this.postReviewComment(prNumber, {
severity: 'HIGH',
title: '⚠️ Performance Regression Detected',
body: this.formatRegressionReport(regressions),
suggestions: await this.aiGenerateOptimizations(regressions)
});
}
}
}Scalability Red Flags
可扩展性警示信号
- N+1 Queries, Missing Indexes, Synchronous External Calls
- In-Memory State, Unbounded Collections, Missing Pagination
- No Connection Pooling, No Rate Limiting
python
def detect_n_plus_1_queries(code_ast):
issues = []
for loop in find_loops(code_ast):
db_calls = find_database_calls_in_scope(loop.body)
if len(db_calls) > 0:
issues.append({
'severity': 'HIGH',
'line': loop.line_number,
'message': f'N+1 query: {len(db_calls)} DB calls in loop',
'fix': 'Use eager loading (JOIN) or batch loading'
})
return issues- N+1查询, 缺少索引, 同步外部调用
- 内存内状态, 无界集合, 缺少分页
- 无连接池, 无速率限制
python
def detect_n_plus_1_queries(code_ast):
issues = []
for loop in find_loops(code_ast):
db_calls = find_database_calls_in_scope(loop.body)
if len(db_calls) > 0:
issues.append({
'severity': 'HIGH',
'line': loop.line_number,
'message': f'N+1 query: {len(db_calls)} DB calls in loop',
'fix': 'Use eager loading (JOIN) or batch loading'
})
return issuesReview Comment Generation
审查意见生成
Structured Format
结构化格式
typescript
interface ReviewComment {
path: string; line: number;
severity: 'CRITICAL' | 'HIGH' | 'MEDIUM' | 'LOW' | 'INFO';
category: 'Security' | 'Performance' | 'Bug' | 'Maintainability';
title: string; description: string;
codeExample?: string; references?: string[];
autoFixable: boolean; cwe?: string; cvss?: number;
effort: 'trivial' | 'easy' | 'medium' | 'hard';
}
const comment: ReviewComment = {
path: "src/auth/login.ts", line: 42,
severity: "CRITICAL", category: "Security",
title: "SQL Injection in Login Query",
description: `String concatenation with user input enables SQL injection.
**Attack Vector:** Input 'admin' OR '1'='1' bypasses authentication.
**Impact:** Complete auth bypass, unauthorized access.`,
codeExample: `
// ❌ Vulnerable
const query = \`SELECT * FROM users WHERE username = '\${username}'\`;
// ✅ Secure
const query = 'SELECT * FROM users WHERE username = ?';
const result = await db.execute(query, [username]);
`,
references: ["https://cwe.mitre.org/data/definitions/89.html"],
autoFixable: false, cwe: "CWE-89", cvss: 9.8, effort: "easy"
};typescript
interface ReviewComment {
path: string; line: number;
severity: 'CRITICAL' | 'HIGH' | 'MEDIUM' | 'LOW' | 'INFO';
category: 'Security' | 'Performance' | 'Bug' | 'Maintainability';
title: string; description: string;
codeExample?: string; references?: string[];
autoFixable: boolean; cwe?: string; cvss?: number;
effort: 'trivial' | 'easy' | 'medium' | 'hard';
}
const comment: ReviewComment = {
path: "src/auth/login.ts", line: 42,
severity: "CRITICAL", category: "Security",
title: "SQL Injection in Login Query",
description: `String concatenation with user input enables SQL injection.
**Attack Vector:** Input 'admin' OR '1'='1' bypasses authentication.
**Impact:** Complete auth bypass, unauthorized access.`,
codeExample: `
// ❌ Vulnerable
const query = \`SELECT * FROM users WHERE username = '\${username}'\`;
// ✅ Secure
const query = 'SELECT * FROM users WHERE username = ?';
const result = await db.execute(query, [username]);
`,
references: ["https://cwe.mitre.org/data/definitions/89.html"],
autoFixable: false, cwe: "CWE-89", cvss: 9.8, effort: "easy"
};CI/CD Integration
CI/CD集成
GitHub Actions
GitHub Actions
yaml
name: AI Code Review
on:
pull_request:
types: [opened, synchronize, reopened]
jobs:
ai-review:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Static Analysis
run: |
sonar-scanner -Dsonar.pullrequest.key=${{ github.event.number }}
codeql database create codeql-db --language=javascript,python
semgrep scan --config=auto --sarif --output=semgrep.sarif
- name: AI-Enhanced Review (GPT-5)
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
run: |
python scripts/ai_review.py \
--pr-number ${{ github.event.number }} \
--model gpt-4o \
--static-analysis-results codeql.sarif,semgrep.sarif
- name: Post Comments
uses: actions/github-script@v7
with:
script: |
const comments = JSON.parse(fs.readFileSync('review-comments.json'));
for (const comment of comments) {
await github.rest.pulls.createReviewComment({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: context.issue.number,
body: comment.body, path: comment.path, line: comment.line
});
}
- name: Quality Gate
run: |
CRITICAL=$(jq '[.[] | select(.severity == "CRITICAL")] | length' review-comments.json)
if [ $CRITICAL -gt 0 ]; then
echo "❌ Found $CRITICAL critical issues"
exit 1
fiyaml
name: AI Code Review
on:
pull_request:
types: [opened, synchronize, reopened]
jobs:
ai-review:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Static Analysis
run: |
sonar-scanner -Dsonar.pullrequest.key=${{ github.event.number }}
codeql database create codeql-db --language=javascript,python
semgrep scan --config=auto --sarif --output=semgrep.sarif
- name: AI-Enhanced Review (GPT-5)
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
run: |
python scripts/ai_review.py \
--pr-number ${{ github.event.number }} \
--model gpt-4o \
--static-analysis-results codeql.sarif,semgrep.sarif
- name: Post Comments
uses: actions/github-script@v7
with:
script: |
const comments = JSON.parse(fs.readFileSync('review-comments.json'));
for (const comment of comments) {
await github.rest.pulls.createReviewComment({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: context.issue.number,
body: comment.body, path: comment.path, line: comment.line
});
}
- name: Quality Gate
run: |
CRITICAL=$(jq '[.[] | select(.severity == "CRITICAL")] | length' review-comments.json)
if [ $CRITICAL -gt 0 ]; then
echo "❌ Found $CRITICAL critical issues"
exit 1
fiComplete Example: AI Review Automation
完整示例:AI审查自动化
python
#!/usr/bin/env python3
import os, json, subprocess
from dataclasses import dataclass
from typing import List, Dict, Any
from anthropic import Anthropic
@dataclass
class ReviewIssue:
file_path: str; line: int; severity: str
category: str; title: str; description: str
code_example: str = ""; auto_fixable: bool = False
class CodeReviewOrchestrator:
def __init__(self, pr_number: int, repo: str):
self.pr_number = pr_number; self.repo = repo
self.github_token = os.environ['GITHUB_TOKEN']
self.anthropic_client = Anthropic(api_key=os.environ['ANTHROPIC_API_KEY'])
self.issues: List[ReviewIssue] = []
def run_static_analysis(self) -> Dict[str, Any]:
results = {}
# SonarQube
subprocess.run(['sonar-scanner', f'-Dsonar.projectKey={self.repo}'], check=True)
# Semgrep
semgrep_output = subprocess.check_output(['semgrep', 'scan', '--config=auto', '--json'])
results['semgrep'] = json.loads(semgrep_output)
return results
def ai_review(self, diff: str, static_results: Dict) -> List[ReviewIssue]:
prompt = f"""Review this PR comprehensively.
**Diff:** {diff[:15000]}
**Static Analysis:** {json.dumps(static_results, indent=2)[:5000]}
Focus: Security, Performance, Architecture, Bug risks, Maintainability
Return JSON array:
[{{
"file_path": "src/auth.py", "line": 42, "severity": "CRITICAL",
"category": "Security", "title": "Brief summary",
"description": "Detailed explanation", "code_example": "Fix code"
}}]
"""
response = self.anthropic_client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=8000, temperature=0.2,
messages=[{"role": "user", "content": prompt}]
)
content = response.content[0].text
if '```json' in content:
content = content.split('```json')[1].split('```')[0]
return [ReviewIssue(**issue) for issue in json.loads(content.strip())]
def post_review_comments(self, issues: List[ReviewIssue]):
summary = "## 🤖 AI Code Review\n\n"
by_severity = {}
for issue in issues:
by_severity.setdefault(issue.severity, []).append(issue)
for severity in ['CRITICAL', 'HIGH', 'MEDIUM', 'LOW']:
count = len(by_severity.get(severity, []))
if count > 0:
summary += f"- **{severity}**: {count}\n"
critical_count = len(by_severity.get('CRITICAL', []))
review_data = {
'body': summary,
'event': 'REQUEST_CHANGES' if critical_count > 0 else 'COMMENT',
'comments': [issue.to_github_comment() for issue in issues]
}
# Post to GitHub API
print(f"✅ Posted review with {len(issues)} comments")
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--pr-number', type=int, required=True)
parser.add_argument('--repo', required=True)
args = parser.parse_args()
reviewer = CodeReviewOrchestrator(args.pr_number, args.repo)
static_results = reviewer.run_static_analysis()
diff = reviewer.get_pr_diff()
ai_issues = reviewer.ai_review(diff, static_results)
reviewer.post_review_comments(ai_issues)python
#!/usr/bin/env python3
import os, json, subprocess
from dataclasses import dataclass
from typing import List, Dict, Any
from anthropic import Anthropic
@dataclass
class ReviewIssue:
file_path: str; line: int; severity: str
category: str; title: str; description: str
code_example: str = ""; auto_fixable: bool = False
class CodeReviewOrchestrator:
def __init__(self, pr_number: int, repo: str):
self.pr_number = pr_number; self.repo = repo
self.github_token = os.environ['GITHUB_TOKEN']
self.anthropic_client = Anthropic(api_key=os.environ['ANTHROPIC_API_KEY'])
self.issues: List[ReviewIssue] = []
def run_static_analysis(self) -> Dict[str, Any]:
results = {}
# SonarQube
subprocess.run(['sonar-scanner', f'-Dsonar.projectKey={self.repo}'], check=True)
# Semgrep
semgrep_output = subprocess.check_output(['semgrep', 'scan', '--config=auto', '--json'])
results['semgrep'] = json.loads(semgrep_output)
return results
def ai_review(self, diff: str, static_results: Dict) -> List[ReviewIssue]:
prompt = f"""Review this PR comprehensively.
**Diff:** {diff[:15000]}
**Static Analysis:** {json.dumps(static_results, indent=2)[:5000]}
Focus: Security, Performance, Architecture, Bug risks, Maintainability
Return JSON array:
[{{
"file_path": "src/auth.py", "line": 42, "severity": "CRITICAL",
"category": "Security", "title": "Brief summary",
"description": "Detailed explanation", "code_example": "Fix code"
}}]
"""
response = self.anthropic_client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=8000, temperature=0.2,
messages=[{"role": "user", "content": prompt}]
)
content = response.content[0].text
if '```json' in content:
content = content.split('```json')[1].split('```')[0]
return [ReviewIssue(**issue) for issue in json.loads(content.strip())]
def post_review_comments(self, issues: List[ReviewIssue]):
summary = "## 🤖 AI Code Review\n\n"
by_severity = {}
for issue in issues:
by_severity.setdefault(issue.severity, []).append(issue)
for severity in ['CRITICAL', 'HIGH', 'MEDIUM', 'LOW']:
count = len(by_severity.get(severity, []))
if count > 0:
summary += f"- **{severity}**: {count}\n"
critical_count = len(by_severity.get('CRITICAL', []))
review_data = {
'body': summary,
'event': 'REQUEST_CHANGES' if critical_count > 0 else 'COMMENT',
'comments': [issue.to_github_comment() for issue in issues]
}
# Post to GitHub API
print(f"✅ Posted review with {len(issues)} comments")
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--pr-number', type=int, required=True)
parser.add_argument('--repo', required=True)
args = parser.parse_args()
reviewer = CodeReviewOrchestrator(args.pr_number, args.repo)
static_results = reviewer.run_static_analysis()
diff = reviewer.get_pr_diff()
ai_issues = reviewer.ai_review(diff, static_results)
reviewer.post_review_comments(ai_issues)Summary
总结
Comprehensive AI code review combining:
- Multi-tool static analysis (SonarQube, CodeQL, Semgrep)
- State-of-the-art LLMs (GPT-5, Claude 4.5 Sonnet)
- Seamless CI/CD integration (GitHub Actions, GitLab, Azure DevOps)
- 30+ language support with language-specific linters
- Actionable review comments with severity and fix examples
- DORA metrics tracking for review effectiveness
- Quality gates preventing low-quality code
- Auto-test generation via Qodo/CodiumAI
Use this tool to transform code review from manual process to automated AI-assisted quality assurance catching issues early with instant feedback.
全面的AI代码审查结合了:
- 多工具静态分析(SonarQube、CodeQL、Semgrep)
- 最先进的大语言模型(GPT-5、Claude 4.5 Sonnet)
- 无缝的CI/CD集成(GitHub Actions、GitLab、Azure DevOps)
- 支持30+语言,搭配特定语言的代码检查工具
- 包含严重程度和修复示例的可操作审查意见
- 跟踪审查有效性的DORA指标
- 阻止低质量代码的质量门
- 通过Qodo/CodiumAI自动生成测试
使用此工具将代码审查从手动流程转变为自动化AI辅助的质量保证,提前发现问题并提供即时反馈。