{"id":"662e052d-497e-4ad5-8e69-1cf1d4b9af8f","shortId":"aKHTsZ","kind":"skill","title":"performance-testing-review-ai-review","tagline":"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","description":"# AI-Powered Code Review Specialist\n\nYou 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.\n\n## Use this skill when\n\n- Working on ai-powered code review specialist tasks or workflows\n- Needing guidance, best practices, or checklists for ai-powered code review specialist\n\n## Do not use this skill when\n\n- The task is unrelated to ai-powered code review specialist\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources/implementation-playbook.md`.\n\n## Context\n\nMulti-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.\n\n## Requirements\n\nReview: **$ARGUMENTS**\n\nPerform comprehensive analysis: security, performance, architecture, maintainability, testing, and AI/ML-specific concerns. Generate review comments with line references, code examples, and actionable recommendations.\n\n## Automated Code Review Workflow\n\n### Initial Triage\n1. Parse diff to determine modified files and affected components\n2. Match file types to optimal static analysis tools\n3. Scale analysis based on PR size (superficial >1000 lines, deep <200 lines)\n4. Classify change type: feature, bug fix, refactoring, or breaking change\n\n### Multi-Tool Static Analysis\nExecute in parallel:\n- **CodeQL**: Deep vulnerability analysis (SQL injection, XSS, auth bypasses)\n- **SonarQube**: Code smells, complexity, duplication, maintainability\n- **Semgrep**: Organization-specific rules and security policies\n- **Snyk/Dependabot**: Supply chain security\n- **GitGuardian/TruffleHog**: Secret detection\n\n### AI-Assisted Review\n```python\n# Context-aware review prompt for Claude 4.5 Sonnet\nreview_prompt = f\"\"\"\nYou are reviewing a pull request for a {language} {project_type} application.\n\n**Change Summary:** {pr_description}\n**Modified Code:** {code_diff}\n**Static Analysis:** {sonarqube_issues}, {codeql_alerts}\n**Architecture:** {system_architecture_summary}\n\nFocus on:\n1. Security vulnerabilities missed by static tools\n2. Performance implications at scale\n3. Edge cases and error handling gaps\n4. API contract compatibility\n5. Testability and missing coverage\n6. Architectural alignment\n\nFor each issue:\n- Specify file path and line numbers\n- Classify severity: CRITICAL/HIGH/MEDIUM/LOW\n- Explain problem (1-2 sentences)\n- Provide concrete fix example\n- Link relevant documentation\n\nFormat as JSON array.\n\"\"\"\n```\n\n### Model Selection (2025)\n- **Fast reviews (<200 lines)**: GPT-4o-mini or Claude 4.5 Haiku\n- **Deep reasoning**: Claude 4.5 Sonnet or GPT-4.5 (200K+ tokens)\n- **Code generation**: GitHub Copilot or Qodo\n- **Multi-language**: Qodo or CodeAnt AI (30+ languages)\n\n### Review Routing\n```typescript\ninterface ReviewRoutingStrategy {\n  async routeReview(pr: PullRequest): Promise<ReviewEngine> {\n    const metrics = await this.analyzePRComplexity(pr);\n\n    if (metrics.filesChanged > 50 || metrics.linesChanged > 1000) {\n      return new HumanReviewRequired(\"Too large for automation\");\n    }\n\n    if (metrics.securitySensitive || metrics.affectsAuth) {\n      return new AIEngine(\"claude-3.7-sonnet\", {\n        temperature: 0.1,\n        maxTokens: 4000,\n        systemPrompt: SECURITY_FOCUSED_PROMPT\n      });\n    }\n\n    if (metrics.testCoverageGap > 20) {\n      return new QodoEngine({ mode: \"test-generation\", coverageTarget: 80 });\n    }\n\n    return new AIEngine(\"gpt-4o\", { temperature: 0.3, maxTokens: 2000 });\n  }\n}\n```\n\n## Architecture Analysis\n\n### Architectural Coherence\n1. **Dependency Direction**: Inner layers don't depend on outer layers\n2. **SOLID Principles**:\n   - Single Responsibility, Open/Closed, Liskov Substitution\n   - Interface Segregation, Dependency Inversion\n3. **Anti-patterns**:\n   - Singleton (global state), God objects (>500 lines, >20 methods)\n   - Anemic models, Shotgun surgery\n\n### Microservices Review\n```go\ntype MicroserviceReviewChecklist struct {\n    CheckServiceCohesion       bool  // Single capability per service?\n    CheckDataOwnership         bool  // Each service owns database?\n    CheckAPIVersioning         bool  // Semantic versioning?\n    CheckBackwardCompatibility bool  // Breaking changes flagged?\n    CheckCircuitBreakers       bool  // Resilience patterns?\n    CheckIdempotency           bool  // Duplicate event handling?\n}\n\nfunc (r *MicroserviceReviewer) AnalyzeServiceBoundaries(code string) []Issue {\n    issues := []Issue{}\n\n    if detectsSharedDatabase(code) {\n        issues = append(issues, Issue{\n            Severity: \"HIGH\",\n            Category: \"Architecture\",\n            Message: \"Services sharing database violates bounded context\",\n            Fix: \"Implement database-per-service with eventual consistency\",\n        })\n    }\n\n    if hasBreakingAPIChanges(code) && !hasDeprecationWarnings(code) {\n        issues = append(issues, Issue{\n            Severity: \"CRITICAL\",\n            Category: \"API Design\",\n            Message: \"Breaking change without deprecation period\",\n            Fix: \"Maintain backward compatibility via versioning (v1, v2)\",\n        })\n    }\n\n    return issues\n}\n```\n\n## Security Vulnerability Detection\n\n### Multi-Layered Security\n**SAST Layer**: CodeQL, Semgrep, Bandit/Brakeman/Gosec\n\n**AI-Enhanced Threat Modeling**:\n```python\nsecurity_analysis_prompt = \"\"\"\nAnalyze authentication code for vulnerabilities:\n{code_snippet}\n\nCheck for:\n1. Authentication bypass, broken access control (IDOR)\n2. JWT token validation flaws\n3. Session fixation/hijacking, timing attacks\n4. Missing rate limiting, insecure password storage\n5. Credential stuffing protection gaps\n\nProvide: CWE identifier, CVSS score, exploit scenario, remediation code\n\"\"\"\n\nfindings = claude.analyze(security_analysis_prompt, temperature=0.1)\n```\n\n**Secret Scanning**:\n```bash\ntrufflehog git file://. --json | \\\n  jq '.[] | select(.Verified == true) | {\n    secret_type: .DetectorName,\n    file: .SourceMetadata.Data.Filename,\n    severity: \"CRITICAL\"\n  }'\n```\n\n### OWASP Top 10 (2025)\n1. **A01 - Broken Access Control**: Missing authorization, IDOR\n2. **A02 - Cryptographic Failures**: Weak hashing, insecure RNG\n3. **A03 - Injection**: SQL, NoSQL, command injection via taint analysis\n4. **A04 - Insecure Design**: Missing threat modeling\n5. **A05 - Security Misconfiguration**: Default credentials\n6. **A06 - Vulnerable Components**: Snyk/Dependabot for CVEs\n7. **A07 - Authentication Failures**: Weak session management\n8. **A08 - Data Integrity Failures**: Unsigned JWTs\n9. **A09 - Logging Failures**: Missing audit logs\n10. **A10 - SSRF**: Unvalidated user-controlled URLs\n\n## Performance Review\n\n### Performance Profiling\n```javascript\nclass PerformanceReviewAgent {\n  async analyzePRPerformance(prNumber) {\n    const baseline = await this.loadBaselineMetrics('main');\n    const prBranch = await this.runBenchmarks(`pr-${prNumber}`);\n\n    const regressions = this.detectRegressions(baseline, prBranch, {\n      cpuThreshold: 10, memoryThreshold: 15, latencyThreshold: 20\n    });\n\n    if (regressions.length > 0) {\n      await this.postReviewComment(prNumber, {\n        severity: 'HIGH',\n        title: '⚠️ Performance Regression Detected',\n        body: this.formatRegressionReport(regressions),\n        suggestions: await this.aiGenerateOptimizations(regressions)\n      });\n    }\n  }\n}\n```\n\n### Scalability Red Flags\n- **N+1 Queries**, **Missing Indexes**, **Synchronous External Calls**\n- **In-Memory State**, **Unbounded Collections**, **Missing Pagination**\n- **No Connection Pooling**, **No Rate Limiting**\n\n```python\ndef detect_n_plus_1_queries(code_ast):\n    issues = []\n    for loop in find_loops(code_ast):\n        db_calls = find_database_calls_in_scope(loop.body)\n        if len(db_calls) > 0:\n            issues.append({\n                'severity': 'HIGH',\n                'line': loop.line_number,\n                'message': f'N+1 query: {len(db_calls)} DB calls in loop',\n                'fix': 'Use eager loading (JOIN) or batch loading'\n            })\n    return issues\n```\n\n## Review Comment Generation\n\n### Structured Format\n```typescript\ninterface ReviewComment {\n  path: string; line: number;\n  severity: 'CRITICAL' | 'HIGH' | 'MEDIUM' | 'LOW' | 'INFO';\n  category: 'Security' | 'Performance' | 'Bug' | 'Maintainability';\n  title: string; description: string;\n  codeExample?: string; references?: string[];\n  autoFixable: boolean; cwe?: string; cvss?: number;\n  effort: 'trivial' | 'easy' | 'medium' | 'hard';\n}\n\nconst comment: ReviewComment = {\n  path: \"src/auth/login.ts\", line: 42,\n  severity: \"CRITICAL\", category: \"Security\",\n  title: \"SQL Injection in Login Query\",\n  description: `String concatenation with user input enables SQL injection.\n**Attack Vector:** Input 'admin' OR '1'='1' bypasses authentication.\n**Impact:** Complete auth bypass, unauthorized access.`,\n  codeExample: `\n// ❌ Vulnerable\nconst query = \\`SELECT * FROM users WHERE username = '\\${username}'\\`;\n\n// ✅ Secure\nconst query = 'SELECT * FROM users WHERE username = ?';\nconst result = await db.execute(query, [username]);\n  `,\n  references: [\"https://cwe.mitre.org/data/definitions/89.html\"],\n  autoFixable: false, cwe: \"CWE-89\", cvss: 9.8, effort: \"easy\"\n};\n```\n\n## CI/CD Integration\n\n### GitHub Actions\n```yaml\nname: AI Code Review\non:\n  pull_request:\n    types: [opened, synchronize, reopened]\n\njobs:\n  ai-review:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/checkout@v4\n\n      - name: Static Analysis\n        run: |\n          sonar-scanner -Dsonar.pullrequest.key=${{ github.event.number }}\n          codeql database create codeql-db --language=javascript,python\n          semgrep scan --config=auto --sarif --output=semgrep.sarif\n\n      - name: AI-Enhanced Review (GPT-5)\n        env:\n          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}\n        run: |\n          python scripts/ai_review.py \\\n            --pr-number ${{ github.event.number }} \\\n            --model gpt-4o \\\n            --static-analysis-results codeql.sarif,semgrep.sarif\n\n      - name: Post Comments\n        uses: actions/github-script@v7\n        with:\n          script: |\n            const comments = JSON.parse(fs.readFileSync('review-comments.json'));\n            for (const comment of comments) {\n              await github.rest.pulls.createReviewComment({\n                owner: context.repo.owner,\n                repo: context.repo.repo,\n                pull_number: context.issue.number,\n                body: comment.body, path: comment.path, line: comment.line\n              });\n            }\n\n      - name: Quality Gate\n        run: |\n          CRITICAL=$(jq '[.[] | select(.severity == \"CRITICAL\")] | length' review-comments.json)\n          if [ $CRITICAL -gt 0 ]; then\n            echo \"❌ Found $CRITICAL critical issues\"\n            exit 1\n          fi\n```\n\n## Complete Example: AI Review Automation\n\n```python\n#!/usr/bin/env python3\nimport os, json, subprocess\nfrom dataclasses import dataclass\nfrom typing import List, Dict, Any\nfrom anthropic import Anthropic\n\n@dataclass\nclass ReviewIssue:\n    file_path: str; line: int; severity: str\n    category: str; title: str; description: str\n    code_example: str = \"\"; auto_fixable: bool = False\n\nclass CodeReviewOrchestrator:\n    def __init__(self, pr_number: int, repo: str):\n        self.pr_number = pr_number; self.repo = repo\n        self.github_token = os.environ['GITHUB_TOKEN']\n        self.anthropic_client = Anthropic(api_key=os.environ['ANTHROPIC_API_KEY'])\n        self.issues: List[ReviewIssue] = []\n\n    def run_static_analysis(self) -> Dict[str, Any]:\n        results = {}\n\n        # SonarQube\n        subprocess.run(['sonar-scanner', f'-Dsonar.projectKey={self.repo}'], check=True)\n\n        # Semgrep\n        semgrep_output = subprocess.check_output(['semgrep', 'scan', '--config=auto', '--json'])\n        results['semgrep'] = json.loads(semgrep_output)\n\n        return results\n\n    def ai_review(self, diff: str, static_results: Dict) -> List[ReviewIssue]:\n        prompt = f\"\"\"Review this PR comprehensively.\n\n**Diff:** {diff[:15000]}\n**Static Analysis:** {json.dumps(static_results, indent=2)[:5000]}\n\nFocus: Security, Performance, Architecture, Bug risks, Maintainability\n\nReturn JSON array:\n[{{\n  \"file_path\": \"src/auth.py\", \"line\": 42, \"severity\": \"CRITICAL\",\n  \"category\": \"Security\", \"title\": \"Brief summary\",\n  \"description\": \"Detailed explanation\", \"code_example\": \"Fix code\"\n}}]\n\"\"\"\n\n        response = self.anthropic_client.messages.create(\n            model=\"claude-3-5-sonnet-20241022\",\n            max_tokens=8000, temperature=0.2,\n            messages=[{\"role\": \"user\", \"content\": prompt}]\n        )\n\n        content = response.content[0].text\n        if '```json' in content:\n            content = content.split('```json')[1].split('```')[0]\n\n        return [ReviewIssue(**issue) for issue in json.loads(content.strip())]\n\n    def post_review_comments(self, issues: List[ReviewIssue]):\n        summary = \"## 🤖 AI Code Review\\n\\n\"\n        by_severity = {}\n        for issue in issues:\n            by_severity.setdefault(issue.severity, []).append(issue)\n\n        for severity in ['CRITICAL', 'HIGH', 'MEDIUM', 'LOW']:\n            count = len(by_severity.get(severity, []))\n            if count > 0:\n                summary += f\"- **{severity}**: {count}\\n\"\n\n        critical_count = len(by_severity.get('CRITICAL', []))\n        review_data = {\n            'body': summary,\n            'event': 'REQUEST_CHANGES' if critical_count > 0 else 'COMMENT',\n            'comments': [issue.to_github_comment() for issue in issues]\n        }\n\n        # Post to GitHub API\n        print(f\"✅ Posted review with {len(issues)} comments\")\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--pr-number', type=int, required=True)\n    parser.add_argument('--repo', required=True)\n    args = parser.parse_args()\n\n    reviewer = CodeReviewOrchestrator(args.pr_number, args.repo)\n    static_results = reviewer.run_static_analysis()\n    diff = reviewer.get_pr_diff()\n    ai_issues = reviewer.ai_review(diff, static_results)\n    reviewer.post_review_comments(ai_issues)\n```\n\n## Summary\n\nComprehensive AI code review combining:\n1. Multi-tool static analysis (SonarQube, CodeQL, Semgrep)\n2. State-of-the-art LLMs (GPT-5, Claude 4.5 Sonnet)\n3. Seamless CI/CD integration (GitHub Actions, GitLab, Azure DevOps)\n4. 30+ language support with language-specific linters\n5. Actionable review comments with severity and fix examples\n6. DORA metrics tracking for review effectiveness\n7. Quality gates preventing low-quality code\n8. Auto-test generation via Qodo/CodiumAI\n\nUse this tool to transform code review from manual process to automated AI-assisted quality assurance catching issues early with instant feedback.\n\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.","tags":["performance","testing","review","antigravity","awesome","skills","sickn33","agent-skills","agentic-skills","ai-agent-skills","ai-agents","ai-coding"],"capabilities":["skill","source-sickn33","skill-performance-testing-review-ai-review","topic-agent-skills","topic-agentic-skills","topic-ai-agent-skills","topic-ai-agents","topic-ai-coding","topic-ai-workflows","topic-antigravity","topic-antigravity-skills","topic-claude-code","topic-claude-code-skills","topic-codex-cli","topic-codex-skills"],"categories":["antigravity-awesome-skills"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/sickn33/antigravity-awesome-skills/performance-testing-review-ai-review","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"cli":"npx skills add sickn33/antigravity-awesome-skills","source_repo":"https://github.com/sickn33/antigravity-awesome-skills","install_from":"skills.sh"}},"qualityScore":"0.700","qualityRationale":"deterministic score 0.70 from registry signals: · indexed on github topic:agent-skills · 34616 github stars · SKILL.md body (15,602 chars)","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill-github:v1","enrichmentVersion":1,"enrichedAt":"2026-04-23T00:51:23.242Z","embedding":null,"createdAt":"2026-04-18T21:42:19.738Z","updatedAt":"2026-04-23T00:51:23.242Z","lastSeenAt":"2026-04-23T00:51:23.242Z","tsv":"'+1':907,967 '-2':416 '-20241022':1431 '-3':1428 '-3.7':503 '-4.5':451 '-5':35,71,1167,1429,1618 '-89':1101 '/data/definitions/89.html':1096 '/usr/bin/env':1255 '0':886,957,1239,1444,1455,1501,1522 '0.1':506,755 '0.2':1436 '0.3':532 '1':240,370,415,539,711,777,933,1059,1060,1247,1453,1601 '10':775,844,879 '1000':267,488 '15':881 '15000':1386 '2':250,377,550,718,785,1393,1610 '20':515,573,883 '200':270,434 '2000':534 '200k':452 '2025':431,776 '3':259,382,562,723,793,1622 '30':196,467,1632 '4':272,389,728,803,1631 '4.5':73,333,442,447,1620 '4000':508 '42':1034,1409 '4o':438,530,1185 '5':393,735,810,1640 '50':486 '500':571 '5000':1394 '6':398,816,1649 '7':823,1656 '8':830,1664 '80':524 '8000':1434 '9':837 '9.8':1103 'a01':778 'a02':786 'a03':794 'a04':804 'a05':811 'a06':817 'a07':824 'a08':831 'a09':838 'a10':845 'access':715,780,1068 'across':195 'action':160,232,1109,1627,1641 'actions/checkout':1134 'actions/github-script':1196 'admin':1057 'affect':248 'ai':5,12,29,38,48,65,97,113,130,205,322,466,694,1112,1124,1163,1251,1368,1473,1583,1593,1597,1684 'ai-assist':204,321,1683 'ai-enhanc':693,1162 'ai-pow':11,37,47,96,112,129 'ai-review':1123 'ai/ml-specific':221 'aiengin':501,527 'alert':363 'align':400 'analysi':20,56,202,214,257,261,287,294,359,536,700,752,802,1138,1188,1334,1388,1578,1606 'analyz':702 'analyzeprperform':860 'analyzeserviceboundari':618 'anem':575 'anthrop':1272,1274,1321,1325 'anti':564 'anti-pattern':563 'api':390,663,1170,1173,1322,1326,1536 'append':628,657,1486 'appli':152 'applic':349 'architectur':192,217,364,366,399,535,537,634,1398 'arg':1566,1568 'argpars':1549 'argparse.argumentparser':1551 'args.pr':1571 'args.repo':1573 'argument':211,1553,1562 'array':428,1404 'art':1615 'ask':1727 'assist':206,323,1685 'assur':1687 'ast':936,944 'async':474,859 'attack':727,1054 'audit':842 'auth':298,1065 'authent':703,712,825,1062 'author':783 'auto':1157,1294,1358,1666 'auto-test':1665 'autofix':1017,1097 'autom':18,54,234,495,1253,1682 'await':481,864,869,887,900,1089,1210 'awar':328 'azur':1629 'backward':673 'bandit/brakeman/gosec':692 'base':201,262 'baselin':863,876 'bash':758 'batch':982 'battl':77 'battle-test':76 'best':107,154 'bodi':896,1219,1514 'bool':586,592,598,602,607,611,1296 'boolean':1018 'bound':640 'boundari':1735 'break':281,603,666 'brief':1415 'broken':714,779 'bug':85,277,1007,1399 'by_severity.get':1497,1510 'by_severity.setdefault':1484 'bypass':299,713,1061,1066 'c':36 'call':913,946,949,956,971,973 'capabl':588 'case':384 'catch':1688 'categori':633,662,1004,1037,1285,1412 'chain':316 'chang':274,282,350,604,667,1518 'check':709,1348 'checkapivers':597 'checkbackwardcompat':601 'checkcircuitbreak':606 'checkdataownership':591 'checkidempot':610 'checklist':110 'checkservicecohes':585 'ci/cd':180,1106,1624 'clarif':1729 'clarifi':146 'class':857,1276,1298 'classifi':273,410 'claud':72,332,441,446,502,1427,1619 'claude.analyze':750 'clear':1702 'client':1320 'code':14,40,50,99,115,132,175,229,235,301,355,356,454,619,626,653,655,704,707,748,935,943,1113,1291,1420,1423,1474,1598,1663,1676 'codeant':465 'codeexampl':1013,1069 'codeql':81,291,362,690,1145,1149,1608 'codeql-db':1148 'codeql.sarif':1190 'coderevieworchestr':1299,1570 'coher':538 'collect':919 'combin':17,53,198,1600 'command':798 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