Goal Recognition Design (GRD) is the problem of designing a domain in a way that will allow easy identification of agents' goals. This paper extends the original GRD problem to domains that are described using hierarchical plans (GRD- PL), and defines the Plan Recognition Design (PRD) problem which is the task of designing a domain using plan libraries in order to facilitate fast identification of an agent's plan. While GRD can help to explain faster which goal the agent is trying to achieve, PRD can help in faster understanding of how the agent is going to achieve its goal. Building on the GRD paradigm, we define for each of these two new problems (GRD-PL and PRD) a measure that quantifies the worst-case distinctiveness of a given planning domain. Then, we study the relation between these measures, showing that the worst case distinctiveness of GRD-PL is a lower bound to the worst case plan distinctiveness of PRD, and that they are equal under certain conditions. Methods for computing each of these measures are presented, and we evaluate these methods in three known hierarchical planning domains from the literature. Results show that in many cases, solving the simpler problem of GRD-PL provides an optimal solution for the PRD problem as well.