Allow users to specify prompt topics they're interested in

Nobody knows everything, and having to continually skip prompts about things one isn't interested in can get tiring. At least two people (one, two) have suggested this on Discord, with a third generally complaining about having to skip so often ("my feedback is "I am not an expert on this topic" for 99% of the skips that I do").

Methods I can think of:

• preset categories, to be set by either the prompt creator, another contributor, or a classification model
  • 👍 simple to implement
  • 👎 inflexible
• a zero-shot classifier (preferably multilingual), users specify categories
  • 👍 flexible, decently accurate (good enough for at least sorting by match probability)
  • 👎 would need to be run for every single prompt-category pair (even the smallest model I could find can only do ~40 pairs per second on HuggingFace servers)
• something similar to Lbl2Vec
  • 👍 each prompt only needs to be calculated once, then it's just cosine similarity
  • 👎 generating the data point for a "label" requires averaging documents; would need to have the user pick prompts they're interested in instead of just writing a word
• a model that maps both prompts and single-word labels to a roughly equivalent latent space, similar to CLIP
  • 👍 best of both worlds
  • 👎 would likely have to be custom-made

The issue names no files, tests, or entry points, so first map where prompts are selected and where user preferences could be stored. Compare the proposed classifier approaches and define a concrete, testable scope; done should mean users can specify interests and prompt matching reflects them.