Mission Essential Competencies (MECs) are in continuing development by the Air Force Research Laboratory for training program enhancement within all mission areas of Air Combat Command. They are unique to specific mission areas such as air combat, suppression, air-to-ground attack, etc. yet provide broad training assessment possibilities in large force team training. MECs are defined as the higher-order individual, team, and inter-team competencies that a fully prepared pilot, crew or flight requires for successful mission completion under adverse conditions in a non-permissive environment. As the definition suggests, MECs are conceptually impractical to use as a means of performance assessment. Decomposing the MECs into their component knowledge, skills and experiences with logical links from observable events represents the most appropriate approach. This paper discusses the approach to decomposition and linkage taken by researchers and subject matter experts to identify and quantify observable events at the task level and to define requirements for observation systems to produce data of sufficient fidelity to support assessment. Air to Air Task-to-MEC mapping links observable events in DMO through knowledge, skill, and supporting competency sets to ultimately make assessments that can be traced to the MEC level. The task mapping product permits objective data from the AFRL's Performance Evaluation Tracking System (PETS) to inform probabilistic assessments of competencies through separate logical constructs for instructional support. During the process, important lessons were learned about the initial MEC process and construct, quality of SME information, and how the development of MECs within a mission area may be improved to facilitate decomposition to observable and assessable levels. Applications of the decomposition product are presented to highlight confidence levels of objective and subjective grading requirements for PETS or similar data collection systems as well as logic techniques developed to bridge areas difficult to assess within existing DMO architectures.