Taste model notes

These notes are injected into the recommender prompt on every run — the LLM sees them alongside your library. Update as your reading model evolves. The baseline (two engines, two gates, rating scale, author priors) is baked into the code; use these to layer new findings, override baseline priors, or add personal rules the code doesn't know.