Information systems, such as task management applications and digital assistants, can help people keep track of tasks of different types and different time durations, ranging from a few minutes to days or weeks. Helping people better manage their tasks and their time are core capabilities of assistive technologies, situated within a broader context of supporting more effective information access and use. Throughout the course of a day, there are typically many short time periods of downtime (e.g., five minutes or less) available to individuals. Microtasks are simple tasks that can be tackled in such short amounts of time. Identifying microtasks in task lists could help people utilize these periods of low activity to make progress on their task backlog. We define actionable tasks as self-contained tasks that need to be completed or acted on. However, not all to-do tasks are actionable. Many task lists are collections of miscellaneous items that can be completed at any time (e.g., books to read, movies to watch), notes (e.g., names, addresses), or the individual items are constituents in a list that is itself a task (e.g., a grocery list). In this article, we introduce the novel challenge of microtask detection, and we present machine-learned models for automatically determining which tasks are actionable and which of these actionable tasks aremicrotasks. Experiments show that our models can accurately identify actionable tasks, accurately detect actionable microtasks, and that we can combine these models to generate a solution that scales microtask detection to all tasks. We discuss our findings in detail, along with their limitations. These findings have implications for the design of systems to help people make the most of their time. [ABSTRACT FROM AUTHOR]