One important role of an agent's motivational system is to choose, at any given moment, which of a number of skills the agent should attempt to improve. Many researchers have suggested “intrinsically motivated” systems that receive internal reward for model learning progress, but for the most part this notion has not been applied with respect to skill competence, or to choose between skills. In this paper we propose an agent motivated to gain competence in its environment by learning a number of skills, addressing head-on the mechanism of competence progress motivation for the purpose of governing the efficient learning of skills. We demonstrate this new approach in a simple illustrative domain and show that it outperforms a naive agent, achieving higher competence faster by focusing attention and learning effort on skills for which progress can be made while ignoring those skills that are already learned or are at the moment too difficult.