In essence, the underlying ultimate purpose of classical deliberative planning systems is to model some world task and generate plans that can then be executed. Execution systems rely on the assumption that there is a built-in library of a variety of plans and primitive actions for performing tasks. Although hard and time consuming, such libraries of execution plans and actions are usually handcoded. Faced with two different systems, a planner and an executor, we analyze in this work the representational map between planning actions and partially-ordered plans and the execution knowledge. We developed algorithms to automatically translate classical planning operators to execution primitive actions, and partially-ordered plans into executable tasks with partially ordered subtasks. We implemented our work using the PRODIGY planner and the RAP execution system. We provide illustrative examples of how partially-ordered plans produced by PRODIGY are translated to Reactive Action Packages suitable for execution by the RAP system.