You add a fixed amount to a logistic regression's intercept. What happens to the decision boundary, recall, precision, ranking metrics and calibration?

It adds the same constant to every logit, a monotonic shift, so the order is unchanged and ranking metrics like AUC stay identical. At a fixed threshold the boundary moves: a positive shift labels more examples positive, so recall rises or holds and precision usually falls. Calibration breaks, since predicted probabilities no longer match observed rates. It's equivalent to moving the threshold, which is how prior correction after resampling works.