AI Engineer · intermediate
AI-Powered Code Reviewer Bot
A GitHub Actions bot that auto-reviews pull requests for style, bugs, and security issues — suggesting refactors.
Project overview
Demonstrates your ability to integrate AI into developer workflows, improving code quality and accelerating development cycles — a highly valued skill in modern software engineering teams.
Real-world use case
A software development team uses this bot to maintain high code quality standards and reduce the manual effort of code reviews.
Tools and technologies
- GitHub Actions
- Python
- OpenAI API
- SonarQube
Skills you can demonstrate
LLM Fine-tuning/Prompting, CI/CD, Static Analysis
Build roadmap
- Create the GitHub Action skeleton
New repo with a workflow that triggers on pull_request. Have it print the PR number first so you know the trigger and permissions work before adding anything clever. - Fetch the PR diff
Use the GitHub API or actions/github-script to pull the changed files and their diff hunks. Save the raw diff as an artifact so you can inspect exactly what the model will see. - Send the diff to the LLM
A Python script that chunks the diff per file, asks the model for bugs, style, and security issues, and returns structured JSON with file, line, severity, and comment. - Post inline review comments
Map each finding to a line in the diff and post it through the review API. Validate first — a comment on an unchanged line is rejected, and the model gets lines wrong. - Add static analysis
Run SonarQube or CodeQL in the same workflow and feed the findings to the model to deduplicate and prioritize. The combined summary comment is what reviewers actually read. - Test on a real repo
Install the action on one of your own projects, open a PR with a deliberate bug, and confirm the bot catches it. Screenshot that review for your portfolio.
Ways to extend the project
Integrate a static analysis tool like SonarQube and have the AI bot summarize its findings and suggest fixes in plain English.
How to showcase your work
- GitHub: Showcase a repository where the bot has provided valuable feedback on pull requests.
- LinkedIn: Share a video demonstrating the bot in action, highlighting its ability to catch errors and suggest improvements.
- Resume: Focus on CI/CD + AI integration.