ALT Characterizing quantum processes is essential for hardware benchmarking, error diagnosis, and algorithm verification. While recent work [PRX QUANTUM 4, 040337 (2023)] extended classical shadows from quantum state to quantum process, enabling efficient single-channel ℰ property prediction, its applicability to composite processes f(ℰ₁, ℰ₂,⋯, ℰₖ) remains unexplored. We introduce shadow engineering, a framework encoding the classical shadows of processes into sparse transfer matrices to predict f(ℰ₁, ℰ₂,⋯, ℰₖ) properties with proven polynomial sample complexity, matching single-channel efficiency while exponentially lower than quantum process tomography. Crucially, this approach repurposes existing ℰₘ-shadow data without physical execution of f(ℰ₁, ℰ₂,⋯, ℰₖ), enabling flexible quantum process characterization with minimal hardware overhead. We demonstrate the framework's effectiveness and practicality on a superconducting quantum processor for typical applications such as error mitigation