code-review-ai-ai-review

Repository: rmyndharis/antigravity-skills
Letzte Quellaktivität: 2. August 2026 um 16:50
Erkannte Sprache von SKILL.md: Englisch
Sterne: 1.258
Forks: 227

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.

npx skills add https://github.com/rmyndharis/antigravity-skills --skill code-review-ai-ai-review

Der Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.

Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

SKILL.md

Quellanweisungen · Schreibgeschützte Vorschau

Zurück zur Suche Zurück zur Installation

agent-orchestration-multi-agent-optimize

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

2026-08-02

antigravity-skills-manager

Global skills manager for Google Antigravity. Explore, search, install, and manage 300+ agent skills from the rmyndharis/antigravity-skills catalog using pure stdlib CLI tools.

2026-08-02

performance-testing-review-ai-review

2026-08-02

Alle aus diesem Repository ansehen

name	code-review-ai-ai-review
description	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

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

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)

#!/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)

code-review-ai-ai-review

Installationsoptionen

Quelldateien prüfen

AI-Powered Code Review Specialist

Use this skill when

Do not use this skill when

Instructions

Context

Requirements

Automated Code Review Workflow

Initial Triage

AI-Powered Code Review Specialist

Use this skill when

Do not use this skill when

Instructions

Context

Requirements

Automated Code Review Workflow

Initial Triage

Multi-Tool Static Analysis

AI-Assisted Review

Model Selection (2025)

Review Routing

Architecture Analysis

Architectural Coherence

Microservices Review

Security Vulnerability Detection

Multi-Layered Security

OWASP Top 10 (2025)

Performance Review

Performance Profiling

Scalability Red Flags

Review Comment Generation

Structured Format

CI/CD Integration

GitHub Actions

Complete Example: AI Review Automation

Summary

code-review-ai-ai-review

Quellinformationen

Installationsoptionen

Quelldateien prüfen

Mehr aus diesem Repository

AI-Powered Code Review Specialist

Use this skill when

Do not use this skill when

Instructions

Context

Requirements

Automated Code Review Workflow

Initial Triage

Mehr aus diesem Repository

AI-Powered Code Review Specialist

Use this skill when

Do not use this skill when

Instructions

Context

Requirements

Automated Code Review Workflow

Initial Triage

Multi-Tool Static Analysis

AI-Assisted Review

Model Selection (2025)

Review Routing

Architecture Analysis

Architectural Coherence

Microservices Review

Security Vulnerability Detection

Multi-Layered Security

OWASP Top 10 (2025)

Performance Review

Performance Profiling

Scalability Red Flags

Review Comment Generation

Structured Format

CI/CD Integration

GitHub Actions

Complete Example: AI Review Automation

Summary