The Single-iteration Ensemble Kalman Smoother (SIEnKS) is now published in Geoscientific Model Development. This work extends traditional methods of ensemble variational data assimilation with a novel approach, optimized for short-range forecast systems. Our prototype technique is shown both to improve the accurracy and to reduce the leading-order cost of ensemble variational smoothing versus traditional 4D approaches. Further studies will follow to extend the prototype to realistic geophysical data assimilation. This prototype development relied on extensive numerical simulation and demonstration with the Julia package DataAssimilationBenchmarks.jl currently in open review in the Journal of Open Source Software.