# Code Review Ai Ai Review

> 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, C

- **Type:** Skill
- **Install:** `agentstack add skill-rmyndharis-antigravity-skills-code-review-ai-ai-review`
- **Verified:** Pending review
- **Seller:** [rmyndharis](https://agentstack.voostack.com/s/rmyndharis)
- **Installs:** 0
- **Category:** [Cloud & Infrastructure](https://agentstack.voostack.com/c/cloud-infrastructure)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [rmyndharis](https://github.com/rmyndharis)
- **Source:** https://github.com/rmyndharis/antigravity-skills/tree/main/skills/code-review-ai-ai-review
- **Website:** https://github.com/rmyndharis/antigravity-skills

## Install

```sh
agentstack add skill-rmyndharis-antigravity-skills-code-review-ai-ai-review
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# AI-Powered Code Review Specialist

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.

## Use this skill when

- Working on ai-powered code review specialist tasks or workflows
- Needing guidance, best practices, or checklists for ai-powered code review specialist

## Do not use this skill when

- The task is unrelated to ai-powered code review specialist
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.

## Context

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.

## Requirements

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.

## Automated Code Review Workflow

### Initial Triage
1. Parse diff to determine modified files and affected components
2. Match file types to optimal static analysis tools
3. Scale analysis based on PR size (superficial >1000 lines, deep  {
    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

### Architectural Coherence
1. **Dependency Direction**: Inner layers don't depend on outer layers
2. **SOLID Principles**:
   - Single Responsibility, Open/Closed, Liskov Substitution
   - Interface Segregation, Dependency Inversion
3. **Anti-patterns**:
   - Singleton (global state), God objects (>500 lines, >20 methods)
   - Anemic models, Shotgun surgery

### Microservices Review
```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

### Multi-Layered Security
**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"
  }'
```

### OWASP Top 10 (2025)
1. **A01 - Broken Access Control**: Missing authorization, IDOR
2. **A02 - Cryptographic Failures**: Weak hashing, insecure RNG
3. **A03 - Injection**: SQL, NoSQL, command injection via taint analysis
4. **A04 - Insecure Design**: Missing threat modeling
5. **A05 - Security Misconfiguration**: Default credentials
6. **A06 - Vulnerable Components**: Snyk/Dependabot for CVEs
7. **A07 - Authentication Failures**: Weak session management
8. **A08 - Data Integrity Failures**: Unsigned JWTs
9. **A09 - Logging Failures**: Missing audit logs
10. **A10 - SSRF**: Unvalidated user-controlled URLs

## Performance Review

### Performance Profiling
```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
- **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
```

## Review Comment Generation

### Structured Format
```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

### 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
          fi
```

## Complete Example: AI Review Automation

```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

Comprehensive AI code review combining:
1. Multi-tool static analysis (SonarQube, CodeQL, Semgrep)
2. State-of-the-art LLMs (GPT-5, Claude 4.5 Sonnet)
3. Seamless CI/CD integration (GitHub Actions, GitLab, Azure DevOps)
4. 30+ language support with language-specific linters
5. Actionable review comments with severity and fix examples
6. DORA metrics tracking for review effectiveness
7. Quality gates preventing low-quality code
8. 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.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [rmyndharis](https://github.com/rmyndharis)
- **Source:** [rmyndharis/antigravity-skills](https://github.com/rmyndharis/antigravity-skills)
- **License:** MIT
- **Homepage:** https://github.com/rmyndharis/antigravity-skills

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** yes
- **Shell / process execution:** yes
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: flagged — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-rmyndharis-antigravity-skills-code-review-ai-ai-review
- Seller: https://agentstack.voostack.com/s/rmyndharis
- Browse the marketplace: https://agentstack.voostack.com/browse

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