We study optimal rating design under moral hazard and strategic manipulation. An intermediary observes a noisy indicator of effort and commits to a rating policy that shapes market beliefs and pay. We characterize optimal ratings via concavification of a gain function. Optimal ratings depends on interaction of effort and risk: for activities that raise tail risk, optimal ratings exhibit lower censorship, pooling poor outcomes to insure and encourage risk-taking; for activities that reduce tail risk, upper censorship increases penalties for negligence. In multi-task environments with window dressing, less informative ratings deter manipulation. In redistributive test design, optimal tests exhibit mid censorship.