Build an AI Code Reviewer with GitHub Actions
Catch security flaws and bugs on every pull request - the same approach Google uses at scale
Difficulty
Mildly spicy
Time to complete
90 minutes
Availability
Free
BUILD
What you'll build
Create an automated code review pipeline that uses AI to analyze every pull request. Build a Python script that calls the Gemini API, wire it into GitHub Actions, and post structured feedback as PR comments - catching SQL injection, hardcoded secrets, and bugs before human reviewers even open the diff.
1. Build the AI Review Script
Get a Gemini API key, install google-genai, and create a Python script for code review.
2. Set Up GitHub Secrets
Add your API key as a repository secret for secure credential handling in CI/CD.
3. Create the PR Review Workflow
Build a GitHub Actions workflow that captures diffs and runs your review script.
4. Test with Vulnerable Code
Push intentionally bad code and watch the AI catch SQL injection and hardcoded secrets.
5. Auto-Label PRs by Severity
Parse AI output to apply labels like critical-security based on detected severity.
Your portfolio builds as you work.
Every project documents itself as you go. Finish the work, and your proof is ready to share.
PROJECT
Real world application
Skills you'll learn
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Gemini API Integration
Connect Python scripts to Google AI for intelligent code analysis
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PR-Triggered Workflows
Build GitHub Actions that run on every pull request automatically
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Automated Code Review
Post structured AI feedback as comments on pull requests
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Security Scanning
Detect SQL injection, hardcoded secrets, and command injection
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GitHub Secrets
Store and access API keys securely in CI/CD workflows
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PR Triage Automation
Auto-label pull requests by AI-detected severity level
Tech stack
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GitHub Actions
CI/CD platform for automating workflows on every pull request, push, or schedule.
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Gemini API
Google AI model that analyzes code diffs and returns structured security feedback.
The AI caught a SQL injection vulnerability I completely missed. Having automated review on every PR gives me confidence that security issues get flagged before they hit production.
Marcus Chen
Senior Developer
OUTCOME
Where this leads.
Relevant Jobs
Roles where these skills matter:
- DevOps Engineer
- Platform Engineer
- Security Engineer
- Senior Developer
CI/CD x AI
Build production-grade automation pipelines that catch bugs, enforce quality, and deploy safely - the same workflows that power Google and Amazon engineering teams.
CI/CD x AI
Continue the JourneyFAQs
Everything you need to know
This is Part 2 of the 4-part CI/CD x AI Series. Complete Part 1 first: Your First GitHub Actions Workflow.
Yes, Google AI Studio offers a generous free tier with 1,500 requests per day - more than enough for development and testing. You will not incur any costs completing this NextWork project.
The AI reviewer is trained to catch common security flaws including SQL injection (unsanitized database queries), command injection (subprocess calls with user input), hardcoded API keys and secrets, and insecure configurations. You will test this by intentionally introducing vulnerable code and watching the AI flag it.
GitHub Secrets are encrypted environment variables stored in your repository settings. They are never exposed in logs, never visible to forked repositories, and are injected at runtime using the syntax ${{ secrets.GEMINI_API_KEY }}. This is the same pattern production teams use to protect credentials in CI/CD pipelines.
Google and Amazon engineering teams use label-based triage to prioritize thousands of pull requests per day. The Secret Mission teaches you to parse the AI review output for severity indicators (HIGH, MEDIUM, LOW) and automatically apply labels like 'critical-security' or 'needs-review' to the PR. This transforms your AI reviewer from a passive commenter into an active triage system.
After completing this project, continue the series with Part 3: Detect Stale Code Automatically and Part 4: Auto-Generate Tests for PRs. For more AI-powered DevOps, explore the AWS AI Security Series.
One Project. Real Skills.
90 minutes from now, you'll have completed Build an AI Code Reviewer with GitHub Actions. No prior experience needed. Just step-by-step guidance and a real project for your portfolio.
Mildly spicy level