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code-reviewer

Automate multi-language code reviews with security and static analysis checks.

Updated Feb 10, 2026
Search Tags:#security#quality#best-practices#static-analysis#code-review#analyze#ai-assist
One-click install
npx skills add https://github.com/Jet-labs/jet-pg-admin --skill code-reviewer-jet-labs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-reviewer
Source: https://github.com/Jet-labs/jet-pg-admin/tree/main/.agent/skills/code-reviewer
Command: npx skills add https://github.com/Jet-labs/jet-pg-admin --skill code-reviewer-jet-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI-powered code reviews help teams consistently identify defects, security vulnerabilities, and maintainability gaps across codebases, reducing risk and speeding up delivery.

Core Features & Use Cases

  • AI-assisted multi-language code analysis for PR reviews and QA checks
  • Integration with static analysis and security tools (e.g., SonarQube, CodeQL, Semgrep)
  • Security-focused review with vulnerability assessment and best-practice guidance
  • Performance and reliability checks including bottleneck detection and scalability guidance
  • Configuration and infrastructure review for production-readiness and resilience
  • Team enablement through checklists, templates, and teach-back guidance

Quick Start

Provide a structured, end-to-end code review of a target PR by applying AI analysis, security checks, and best-practice guidance.

Frequently Asked Questions about code-reviewer

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

FAQPage Schema
How do I automate AI-powered code reviews for pull requests?▼

Automate AI-powered code reviews by applying multi-language analysis, security checks, and best-practice guidance to target PRs. This identifies defects and maintainability gaps while providing structured deliverables and actionable checklists to speed up delivery.

How does AI-assisted static analysis detect security vulnerabilities in a multi-language codebase?▼

AI-assisted static analysis detects vulnerabilities by integrating with security tools like SonarQube, CodeQL, and Semgrep. It performs vulnerability assessments and applies best-practice guidance across multi-language codebases to identify risks and improve reliability.

Can I use this code review process for production-readiness assessments and infrastructure configuration checks?▼

Yes, code review processes support production-readiness assessments by evaluating infrastructure configuration for resilience and scalability. They include performance and reliability checks like bottleneck detection to ensure systems are ready for production.

What's the best way to integrate static analysis tools with an AI code review workflow?▼

The best way to integrate static analysis tools is to combine AI analysis with security tools such as SonarQube, CodeQL, and Semgrep. This combination enables comprehensive vulnerability assessments, performance profiling, and maintainability guidance for software teams.

Does AI code review work with existing security testing practices and team checklists?▼

Yes, AI code review works with existing security testing practices and enhances team enablement through checklists, templates, and teach-back guidance. It satisfies requirements for integrating security testing and maintainability guidance across software teams.

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