An expressive renderer must learn two things: how an instrument sounds and how it is played. Quill factorizes them into two separately learned, reusable assets: a lightweight DDSP timbre model and a note-level style library of pitch and energy trajectories, retrieved by articulation-conditioned nearest-neighbor lookup. Either asset can be swapped across instruments without retraining. Cross-rendering across violin, trumpet, and guitar passes all 12 source-attribution checks. In a pre-specified listening study (N=19), listeners track behavior across timbres and rate renders that carry it as more human-like. A paired counterfactual test isolates the articulation-label intervention from note-level confounds. A case study shows that both assets can also be learned from 82 s of rubbed-glass recordings.