Prodigy and Spacy already have support to segmentation of texts into sentences, and tokenization of texts too, you should easily find some documentation on these. I had another example in which I was iterating through my training data and making them available to prodigy using stdin, and had no problems with it.
Then you could use the text classification recipe to classify each sentence. In order to get a great perspective on prodigy, I strongly recommend that you watch the two tutorials on new entity type and insults classifier. They were essential to provide the insights on how the tool works.
As for “marking important words”, the ner.manual recipe could be used for this, since it produces annotation data in the format of text and annotated spans, as already exemplified here and in some of my other threads where I discussed this with @ines and @honnibal.