Combine static analysis with model-assisted code review to assess security, performance, correctness, and architecture. Use when designing review automation or reviewing complex changes.
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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.
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.
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.
Execute in parallel:
# 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:
1. Security vulnerabilities missed by static tools
2. Performance implications at scale
3. Edge cases and error handling gaps
4. API contract compatibility
5. Testability and missing coverage
6. 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.
"""
Pass a model ID enabled for your account as securityModel when configuring security-focused reviews.
interface ReviewRoutingStrategy {
async routeReview(pr: PullRequest, securityModel: string): 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(securityModel, {
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 });
}
}
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
}
SAST Layer: CodeQL, Semgrep, Bandit/Brakeman/Gosec
AI-Enhanced Threat Modeling:
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:
trufflehog git file://. --json | \
jq '.[] | select(.Verified == true) | {
secret_type: .DetectorName,
file: .SourceMetadata.Data.Filename,
severity: "CRITICAL"
}'
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)
});
}
}
}
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
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"
};
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
fi
#!/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-sonnet-4-6",
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)
Comprehensive AI code review combining:
Use this tool to transform code review from manual process to automated AI-assisted quality assurance catching issues early with instant feedback.
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