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Candida auris, an increasingly prevalent fungal pathogen, requires both rapid identification and antifungal susceptibility testing to enable proper treatment. This study introduces digital SHERLOCK (dSHERLOCK), a platform that combines CRISPR/Cas nucleic acid detection, single-template quantification and real-time kinetics monitoring. Assays implemented on this platform display excellent sensitivity to C. auris from major clades 1-4, while maintaining specificity when challenged with common environmental and pathogenic fungi. dSHERLOCK detects C. auris within 20 min in minimally processed swab samples and achieves sensitive quantification (1 c.f.u. µl) within 40 min. To address antifungal susceptibility testing, we develop assays that detect mutations that are commonly associated with azole and echinocandin multidrug resistance. We use machine learning and real-time monitoring of reaction kinetics to achieve highly accurate simultaneous quantification of mutant and wild-type FKS1 SNP alleles in fungal populations with mixed antifungal susceptibility, which would be misdiagnosed as completely susceptible or resistant under standard reaction conditions. Our platform's use of commercially available materials and common laboratory equipment makes C. auris diagnostics widely deployable in global healthcare settings.