Sighs. You presented a challenge for providing a definition for the subjective categorization of a "great programmer." It was my assumption that you did not h

Sighs. You presented a challenge for providing a definition for the subjective categorization of a "great programmer." It was my assumption that you did not have a satisfactory one, based on how you presented this challenge. Perhaps the problem domain which you presented is not immediately recognizable. In defining a concept relating to people, a plausible one will include terms/metrics/observations relevant to people. This implies a continuous domain (as opposed to a discrete one) much like what exists in analog systems. Attempting to apply binary "yes/no" requirements and/or tests when faced with a partially observable universe doesn't work. Which is why AI agents use heuristics such as "confidence factors." And a person with which an organization has little to no history with presents a partially observable universe often limited to their CV. Now, a computer program has to do approximations such as confidence factors because _it_ is a digital entity (assuming a Von Neumann machine[1]). But we, as human beings, are not Von Neumann machines. We are analog. Which implies continuous domain metrics (IOW: a "spectrum"). Hence the fruitless effort of attempting to apply software engineering best practices onto people. However, addressing the problem defined as being "evaluate whether or not this multi-dimensional analog system can be of value to a collaborating set of multi-dimensional analog systems, for some equally nebulous definition of value" may reveal that collecting measurements within the observable outputs of said analog systems has a higher probability of success than one which attempts to apply measurements/practices for an entirely different problem domain. YMMV. 1- https://en.wikipedia.org/wiki/Von_Neumann_architecture https://en.wikipedia.org/wiki/Von_Neumann_architecture