Now I have binary classification problem with positive samples roughly 100 times the number of negative samples. In this case the normal accuracy measure (predict == label) is not a good measure. What other measures there are? Is precision, recall for negative sample fine or F-1 measure the best? If the model is a probability model, is AUC (Area under curve) a good measure?
Any method that uses arbitrary cutoffs and dichotomizes continuous information such as probability of class membership is problematic. And classification accuracy is an improper accuracy scoring rule, being optimized by the wrong model. Concordance probability ($c$-index; ROC area) is a measure of pure discrimination. For an overall measure consider the proper accuracy score known as the Brier score or use a generalized likelihood-based $R^2$ measure.