Bayesian F1

Decision theory considers  the posterior probability to decide which class to chose , or weight using a cost function. However in this setting its objective is the accuracy. Most often we are more interested in more balanced measures such as F1 due to skewed classes. How would be F1 maximizer Bayesian decision mechanism if the distribution is exactly known ? I guess this is already implemented in somewhere , however I didn't face it yet.