In this paper we explore the question of what characterises a desirable plan of action and how such a plan could be computed, in the context of systems that already possess a certain amount of hierarchical domain knowledge. In contrast to past work in this setting, which focuses on generating low-level plans, losing much of the domain knowledge inherent in such systems, we argue that plans ought to be HTN-like or abstract, i.e., re-use and respect the user-supplied know-how in the underlying domain. In doing so, we recognise an intrinsic tension between striving for abstract plans but ensuring that unnecessary actions, not linked to the specific goal to be achieved, are avoided. We explore this tension by characterising the set of “ideal” abstract plans that are non-redundant but maximally abstract, and then develop a more limited yet feasible account in which a given (arbitrary) abstract plan is “specialised” into one such non-redundant plan that is as abstract as possible. We present an algorithm that can compute such specialisations, and analyse the theoretical properties of our proposal.