AI Code Review Automation with GitHub Actions

Overview

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Build an AI-powered code review bot from scratch and publish it to the GitHub Marketplace. This hands-on course walks you through the complete lifecycle of creating a GitHub Action that uses Large Language Models to automatically review pull requests and provide actionable feedback on code quality. You start by exploring why automated code review matters, examining real pull requests in complex projects, and understanding the architecture of AI review pipelines built on GitHub Actions. You then define review criteria using the pmat code quality analysis tool, study existing review actions as reference implementations, and develop prompt engineering strategies that produce useful AI feedback. In the implementation phase, you apply documentation-driven development to plan your action, build it with AI assistance, add tests, and refine through local testing strategies. You deploy the action to GitHub, use it on real pull requests, and confront practical challenges of generative AI including non-deterministic responses. The course concludes with writing clear action documentation and publishing your review bot to the GitHub Marketplace for community distribution.

Syllabus

Building an AI Code Review System

Covers why automate reviews with AI, GitHub Actions architecture, PMAT complexity analysis, review criteria definition, iterative prompt strategy, action creation, testing, and local validation.

Deploying and Publishing AI Code Review

Covers deploying the action to GitHub, using the bot in real pull requests, handling generative AI challenges (hallucination, inconsistency), writing action documentation, and publishing to GitHub Marketplace.

Capstone Project

Build and deploy an AI-powered code review GitHub Action that extends [paiml/pmat-action](https://github.com/paiml/pmat-action) with LLM-based analysis, contextual PR feedback, and GitHub Marketplace publishing. The project covers the complete lifecycle from defining review criteria and prompt strategy through local testing, deployment, and documentation.