Developed an automated reviewer suggestion feature for the apache/texera repository, focusing on streamlining the pull request review process. Leveraging JavaScript, YAML, and GitHub Actions, the solution introduced a CI workflow that analyzes git blame history whenever a pull request is opened or updated. This workflow identifies potential reviewers based on recent contributions to the affected files, then posts suggestions as a comment, clearly distinguishing between committers and non-committers. The system updates its suggestions with each new push, maintaining a clean PR timeline. This automation reduced manual reviewer assignment, improved reviewer coverage, and accelerated code review cycles for the project.