Blaise worked on the sublime-security/sublime-rules repository, focusing on refining the close-the-loop classification logic to address inconsistent behavior in automated threat detection. By adjusting the trigger condition to depend on both abuse reports and user report counts, Blaise improved the accuracy and stability of classification outcomes, reducing mislabeling and the need for manual review. The solution was implemented using YAML for configuration management and automation, with changes tracked for future audits and tuning. This targeted bug fix enhanced the maintainability and traceability of the rules engine, demonstrating a thoughtful approach to evolving complex classification workflows within the project’s automation framework.