The rising interest in complex oxides for energy storage applications calls for the development of efficient computational schemes that enable exploring the vast configurational space of these materials to guide and complement experiments. In this work, we adopt a high-throughput screening method based on density-functional theory to investigate the electronic-structure fingerprints of a specific stoichiometry of lithiated manganese-cobalt-nickel oxide, [Formula: see text], which are relevant for the identification of the material in X-ray spectroscopy experiments. After creating the candidate structures in an automated fashion, we inspect their structural characteristics and electronic properties focusing specifically on the Ni and O contributions to the density of states. To do so, we exploit data analysis schemes that provide us with a metric to classify the considered structures according to the properties of interest, including the oxidation state. Comparison with X-ray absorption spectroscopy measurements confirms the robustness of the developed computational approach and reveals the most likely composition of the probed sample.