If you mean using the posterior of the model as a prior for the next model, there’s been a lot of discussion on this here across multiple threads (see here for example), and the answer is that there is no automatic way of doing it, doing it by hand would require a ton of simplification (ex. we have limited multivariate distributions, so you’d be collapsing the full dimensional posterior into simpler marginals to which you try to find defined distributions that fit well enough and hope you don’t lose too much info in the process), so it’s generally better to try to work out how to model all the data at once rather than iteratively in this manner.
If you mean use the samples from the posterior as literal data input to a second model, that really does not make any inferential sense as you would be able to generate an arbitrarily narrow/confident posterior by simply changing the number of samples provided as input.