It has been known since the work of Foster and Vohra that decision makers that best respond to -fully calibrated- forecasts take actions that guarantee them no swap regret. Unfortunately the computational and data complexity of full distributional calibration is exponential in the dimension of the problem. Using game theoretic techniques, we show how efficiently make high dimensional calibrated forecasts tailored to particular decision makers, sufficient to give them no swap regret guarantees. We'll survey some applications of this technique to playing games with large action spaces and to sequential contract theory.