In a ClickHouse database, a table was created to track data from GitHub Actions using GitHub Webhooks for the ClickHouse organization, providing essential metrics on internal workflow job queues. The data was then used to implement a lambda function that dynamically inflates and deflates workers on demand, improving responsiveness and compute savings. A new system was developed where the desired capacity is calculated based on current runners and jobs in progress/queue, with adjustments made proportionally to deficit or reserve. This approach replaced an old system that relied on CloudWatch alarms and GitHub runner API monitoring, resulting in faster job launching and reduced compute time.