People are increasingly using customizable remotes to interact with devices in new and interesting ways that are influenced by the idiosyncrasies of their behaviors and environments. With the growing use of advanced processors in small consumer electronics, it is becoming more practical to have such remotes execute machine-learning algorithms that can automatically specify the idiosyncrasies. This paper addresses two especially useful and common types of features of personalizable remotes: "task based button grouping" and macros. "Task-based button grouping" addresses clutter and frequent screen switching by only presenting the commands (or buttons) a user needs to accomplish a given active task. Macros allow users to efficiently invoke a sequence of commands across multiple devices that are used in the task. The contributions of this work include: (a) an identification of several usage patterns that show limitations of previous work in task and macro based commands, (b) a set of new algorithms that apply these patterns to address these limitations, and (c) an evaluation of each algorithm using real-world interaction data. We show that our algorithms, which uniquely apply fuzzy techniques and time-based heuristics, can offer a significant improvement from the state-of-the-art in automation and accuracy.