Authorial style transfer is particularly challenging in low-resource scenarios, such as those presented by languages with a distinct socio-digital trajectory like Turkish, where contemporary digital text coexists with under-resourced literary and historical styles. This work addresses this gap through the Dual-Stage Stylometric Imprinting (DSSI) framework, introducing a Rule+Example paradigm for effective style profiling. Evaluated on a corpus of Turkish texts, the approach enables smaller models to achieve up to 90% of large model performance by combining explicit stylistic guidelines with contextual demonstrations. The findings demonstrate altered scaling laws for stylistic tasks and facilitate the practical deployment of personalized style transfer for preserving distinctive writing characteristics.