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Fandry96
Fandry96COMMUNITY
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code-reviewer

A comprehensive code review tool that scans for bugs, vulnerabilities, and inefficiencies.

27|5|Updated Apr 3, 2026
Search Tags:#performance#security#maintainability#ai#review#code#static-analysis
One-click install
npx skills add https://github.com/Fandry96/k3-agentic-skills --skill code-reviewer-fandry96
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-reviewer
Source: https://github.com/Fandry96/k3-agentic-skills/tree/main/skills/code-reviewer
Command: npx skills add https://github.com/Fandry96/k3-agentic-skills --skill code-reviewer-fandry96

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Streamlines and elevates code quality by combining expert human review guidance with AI-powered analysis to catch issues earlier, improve security, performance, and maintainability.

Core Features & Use Cases

  • AI-powered code analysis integrated with leading tools (Trag, Bito, Codiga, GitHub Copilot) to surface issues and recommendations.
  • Comprehensive static analysis coverage (SonarQube, CodeQL, Semgrep, Snyk, Bandit) for security, performance, and quality.
  • Production-grade review workflows including PR analysis, automated comments, and governance checks.
  • Performance and scalability review to detect bottlenecks, memory issues, and optimization opportunities.
  • Configuration & infrastructure review guidance for IaC, containers, and deployment pipelines.
  • Workflow and process guidance for teams: checklists, conventions, and knowledge transfer.
  • Multi-language support and learning materials to uplift engineering teams.

Quick Start

Provide the code sample or PR details and goals to start a structured AI-assisted review.

Frequently Asked Questions about code-reviewer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use AI to review code for security and performance issues?▼

AI code review analyzes your source code and pull requests to surface security vulnerabilities, performance bottlenecks, and maintainability issues. It integrates with tools like SonarQube and Semgrep to provide structured recommendations.

Can I integrate automated code analysis into my CI pipelines?▼

Yes, automated code analysis supports integration into CI pipelines. It applies security, performance, and maintainability checks across multi-repo environments and provides automated comments on pull requests.

Does this code review workflow support infrastructure as code and containers?▼

Yes, the code review workflow includes configuration and infrastructure review guidance for IaC, containers, and deployment pipelines. It helps identify misconfigurations and optimize deployment processes.

What is the best way to perform static analysis on a multi-language codebase?▼

Performing static analysis on a multi-language codebase is best achieved by combining AI-powered analysis with tools like CodeQL and Snyk. This approach ensures comprehensive security and quality coverage across varying languages and sizes.

How do I start an AI-assisted review for a pull request?▼

To start an AI-assisted review, provide the code sample or pull request details along with your review goals. The system then guides a structured analysis covering security, performance, and maintainability checks.

What tools can I combine with AI to detect memory issues and optimize code?▼

You can combine AI with static analysis tools like SonarQube and Bandit to detect memory issues and optimize code. This integration enables performance and scalability reviews to identify bottlenecks.

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