We introduce the Skill Plot, a method that it is directly relevant to a decision maker who must use a diagnostic test. In contrast to ROC curves, the skill curve allows easy graphical inspection of the optimal cutoff or decision rule for a diagnostic test. The skill curve and test also determine whether diagnoses based on this cutoff improve upon a naive forecast (of always present or of always absent). The skill measure makes it easy to directly compare the predictive utility of two different classifiers in an analogy to the area under the curve statistic related to ROC analysis. Finally, this article shows that the skill-based cutoff inferred from the plot is equivalent to the cutoff indicated by optimizing the posterior odds in accordance with Bayesian decision theory. A method for constructing a confidence interval for this optimal point is presented and briefly discussed.