How-to procedures, such as how to plant a garden, are ubiquitous. But one size does not fit all - humans often need to customize these procedural plans according to their specific needs, e.g., planting a garden without pesticides. While LLMs can fluently generate generic procedures, we present the first study on how well LLMs can customize open-domain procedures. We introduce CustomPlans, a probe dataset of customization hints that encodes diverse user needs for open-domain How-to procedures. Using LLMs as CustomizationAgent and ExecutionAgent in different settings, we establish their abilities to perform open-domain procedure customization. Human evaluation shows that using these agents in a Sequential setting is the best, but they are good enough only ~51% of the time. Error analysis shows that LLMs do not sufficiently address user customization needs in their generated procedures.
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