A method for at least one of characterizing and diagnosing a cutaneous condition in at least a subject, the method comprising: at a sample handling network, processing nucleic acid content of each of an aggregate set of samples, from a population of subjects, with a fragmentation operation, a multiplexed amplification operation using a set of primers, a sequencing analysis operation, and an alignment operation,; at a computing system in communication with the sample handling network, generating a microbiome composition dataset and a microbiome functional diversity dataset upon processing of the aggregate set of samples; at the computing system, receiving a supplementary dataset informative of the cutaneous condition within the population of subjects; at the computing system, transforming the supplementary dataset and features extracted from the microbiome composition dataset and the microbiome functional diversity dataset into a characterization model of the cutaneous condition; generating a therapy model, associated with the characterization model, configured to improve a state of the cutaneous condition; and at an output device associated with the subject, promoting a therapy to the subject, upon characterizing the subject with the cutaneous condition according to the characterization model and the therapy model, wherein generating the characterization model comprises performing a statistical analysis to assess a set of microbiome composition features and microbiome functional features having variations across a first subset of the population of subjects exhibiting the cutaneous condition and a second subset of the population of subjects not exhibiting the cutaneous condition, wherein generating the characterization model of the cutaneous condition comprises generating a characterization that is diagnostic of a skin condition, wherein generating the characterization model of the cutaneous condition comprises generating the characterization of at least one of: acne and dermatomyositis, wherein generating the characterization that is diagnostic of acne comprises generating the characterization upon processing the aggregate set of samples and determining presence of features derived from 1) a set of taxa including: Phascolarctobacterium (genus) and 2) a set of functions including a first functional feature related to lepB TPP signal peptidase, and wherein generating the characterization that is diagnostic of dermatomyositis comprises generating the characterization upon processing the aggregate set of samples and determining presence of features derived from 1) a set of taxa including at least one of: Faecalibacterium (genus), Ruminococcaceae (family), and Bacteroidia (class) and 2) a set of functions including an energy metabolism KEGG L3 derived feature, an immune system KEGG L2 derived feature, and a D-Alanine metabolism KEGG L3 derived feature. at a sample handling network, processing nucleic acid content of each of an aggregate set of samples, from a population of subjects, with a fragmentation operation, a multiplexed amplification operation using a set of primers, a sequencing analysis operation, and an alignment operation,; at a computing system in communication with the sample handling network, generating a microbiome composition dataset and a microbiome functional diversity dataset upon processing of the aggregate set of samples; at the computing system, receiving a supplementary dataset informative of the cutaneous condition within the population of subjects; at the computing system, transforming the supplementary dataset and features extracted from the microbiome composition dataset and the microbiome functional diversity dataset into a characterization model of the cutaneous condition; generating a therapy model, associated with the characterization model, configured to improve a state of the cutaneous condition; and at an output device associated with the subject, promoting a therapy to the subject, upon characterizing the subject with the cutaneous condition according to the characterization model and the therapy model, wherein generating the characterization model comprises performing a statistical analysis to assess a set of microbiome composition features and microbiome functional features having variations across a first subset of the population of subjects exhibiting the cutaneous condition and a second subset of the population of subjects not exhibiting the cutaneous condition, wherein generating the characterization model of the cutaneous condition comprises generating a characterization that is diagnostic of a skin condition, wherein generating the characterization model of the cutaneous condition comprises generating the characterization of at least one of: acne and dermatomyositis, wherein generating the characterization that is diagnostic of acne comprises generating the characterization upon processing the aggregate set of samples and determining presence of features derived from 1) a set of taxa including: Phascolarctobacterium (genus) and 2) a set of functions including a first functional feature related to lepB TPP signal peptidase, and wherein generating the characterization that is diagnostic of dermatomyositis comprises generating the characterization upon processing the aggregate set of samples and determining presence of features derived from 1) a set of taxa including at least one of: Faecalibacterium (genus), Ruminococcaceae (family), and Bacteroidia (class) and 2) a set of functions including an energy metabolism KEGG L3 derived feature, an immune system KEGG L2 derived feature, and a D-Alanine metabolism KEGG L3 derived feature.