An essential part in the construction of a mental picture of a situation is our ability to represent, assess and judge on the skill and competency level of a player, an agent, or any entity that makes decisions. Augmented with a suitable reasoning framework, this ability would allow us to diagnose the competency level of a player through real-time monitoring of their actions. The first key question to ask, is how to assess the skill level of a player from a sequence of actions. Putting it differently, if a strategy maps the objectives of an organization into actions, can we use the actions to assess the skill and competency level of a strategic-decision maker? While this question is traditionally answered through psychological and skill-assessment tests, the objective of the current work is to provide an automated, nonintrusive, passive method to evaluate the skill level of players. In this paper, we will present a framework whereby a computational environment is used to study and assess the competency of a decision maker. We use the game of GO to demonstrate the functionality of the environment. Hundreds of human-played GO games are analyzed in a computational environment. A combination of data mining and time-series analysis is developed to monitor and track the competency level of the human players. We then demonstrate that this methodology is successful in diagnosing problems for some players. The methodology is designed to both augment the construction of a situation awareness picture, as well as a training diagnosis tool.