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principal-reviewer

Automate code review workflows with Python static analysis and AWS documentation.

Updated May 12, 2026
Search Tags:#quality assurance#code review#performance#security#static code analysis
One-click install
npx skills add https://github.com/HillBombCreations/vivreal-skills --skill principal-reviewer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: principal-reviewer
Source: https://github.com/HillBombCreations/vivreal-skills/tree/main/vivreal-principal/skills/principal-reviewer
Command: npx skills add https://github.com/HillBombCreations/vivreal-skills --skill principal-reviewer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mcp__plugin_context7_context7__query-docs, mcp__plugin_context7_context7__resolve-library-id, mcp__awslabs_aws-documentation-mcp-server__search_documentation, mcp__awslabs_aws-documentation-mcp-server__read_documentation, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of thorough code reviews, identifying potential issues like correctness, security vulnerabilities, performance bottlenecks, and more, before shipping a code change.

Core Features & Use Cases

  • Automated Code Review: Analyze code across multiple dimensions (correctness, security, performance, etc.).
  • Risk Identification: Highlight and explain issues that may lead to data loss, security breaches, or system crashes.
  • Review Dimensions: Code review across 8 dimensions including correctness, security, performance, scalability, data structures, cloud architecture, reliability, and system design.
  • Feedback Format: Provide structured feedback with ratings and explanations to assist developers in understanding and addressing the identified issues.

Quick Start

Initiate a review using the 'principal-reviewer' skill followed by the path to your codebase or diff.

Frequently Asked Questions about principal-reviewer

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

FAQPage Schema
How do I automate code review across multiple dimensions like security and performance?▼

Automated code review across multiple dimensions is handled by analyzing correctness, security, performance, scalability, data structures, cloud architecture, reliability, and system design. The skill performs static code analysis using Python to identify potential issues and provides structured feedback with ratings.

Can I use static code analysis to check my codebase against AWS architecture guidelines?▼

Yes, static code analysis can check your codebase against AWS architecture guidelines. The skill searches AWS documentation for relevant architectural best practices and applies them alongside your repository changes to ensure adherence to cloud architecture standards.

What's the best way to identify security vulnerabilities and performance bottlenecks before shipping code?▼

Identifying security vulnerabilities and performance bottlenecks before shipping is achieved through an automated review workflow. The skill analyzes code changes in any repository to highlight and explain risks that may lead to data loss, security breaches, or system crashes.

How do I start an automated code review for a specific diff or codebase path?▼

To start an automated code review, initiate the skill followed by the path to your codebase or diff. The tool will then process the code changes and return structured feedback covering eight dimensions of code quality and architectural adherence.

Does automated static code analysis work with any repository format?▼

Automated static code analysis works with code changes in any repository. It handles code changes universally by applying Python-based analysis and Vivreal-wide standards to ensure adherence to performance and architectural best practices regardless of the specific repository.

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