review-code

Analyze GitHub pull request diffs and categorize issues by severity.

Updated Aug 23, 2026

Search Tags:#code quality #automation #diff analysis #code review #pull request #github

Ready to run

npx skills add https://github.com/packbot/agentic-coding-boilerplate --skill review-code-packbot

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: review-code
Source: https://github.com/packbot/agentic-coding-boilerplate/tree/main/.claude/skills/review-code
Command: npx skills add https://github.com/packbot/agentic-coding-boilerplate --skill review-code-packbot

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires gh, and includes scripts (resource) components.

What problem does it solve?

This Skill automates the process of reviewing code changes, particularly for GitHub pull requests, by providing a structured and systematic assessment.

Core Features & Use Cases

Automated PR Analysis: Fetches PR details, checks out the branch, and generates a diff.
Intelligent Diff Assessment: Categorizes diffs by size (small, medium, large) to tailor the review approach.
Systematic Issue Identification: Guides the reviewer through identifying potential issues, verifying their validity in context, and filtering out noise.
Structured Output: Presents findings in a clear, categorized format (Critical, Important, Minor).
Use Case: When a new feature branch is submitted for review, this Skill can pre-process the changes, highlight potential problems, and provide a summarized review to the human reviewer, saving them time and ensuring consistency.

Quick Start

Use the review-code skill to analyze the pull request with number 123.

Frequently Asked Questions about review-code

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

FAQPage Schema

How do I automate code review for a GitHub pull request?▼

The code review automation fetches PR data, checks out branches locally, analyzes the diff, and categorizes potential issues by severity to streamline your workflow.

What is the best way to analyze large pull request diffs efficiently?▼

Large pull request diffs are handled by categorizing diffs by size to tailor the review approach, systematically identifying issues, and filtering out noise for efficient assessment.

Do I need the GitHub CLI installed to review pull request changes?▼

Yes, you need the GitHub CLI installed because the code review process requires integration with GitHub CLI and local Git operations to fetch PR data and generate diffs.

How does automated diff analysis categorize potential code issues?▼

Automated diff analysis categorizes potential code issues into structured severity levels of Critical, Important, and Minor, presenting findings in a clear format after verifying validity in context.

Can I use this to pre-process feature branch changes before human review?▼

Yes, you can use this to pre-process feature branch changes by highlighting potential problems and providing a summarized review to the human reviewer, saving time and ensuring consistency.

What are the limitations of automating code reviews with diff analysis?▼

Automating code reviews with diff analysis requires local Git operations and GitHub CLI integration, meaning it is limited to local execution environments and cannot run natively in isolated CI containers without dependencies.