We present a general framework for setting up Stein's method for multivariate continuous distributions. The approach gives a collection of Stein characterizations, among which we highlight score-Stein operators and kernel-Stein operators. Applications include copu-las and distance between posterior distributions. We give a general explicit construction for Stein kernels for elliptical distributions and discuss Stein kernels in generality, highlighting connections with Fisher information and mass transport. Finally, a goodness-of-fit test based on Stein discrepancies is given.