  As machine learning models become more general, we need to characterise them
in richer, more meaningful ways. We describe a method to infer the cognitive
profile of a system from diverse experimental data. To do so, we introduce
measurement layouts that model how task-instance features interact with system
capabilities to affect performance. These features must be triangulated in
complex ways to be able to infer capabilities from non-populational data -- a
challenge for traditional psychometric and inferential tools. Using the
Bayesian probabilistic programming library PyMC, we infer different cognitive
profiles for agents in two scenarios: 68 actual contestants in the AnimalAI
Olympics and 30 synthetic agents for O-PIAAGETS, an object permanence battery.
We showcase the potential for capability-oriented evaluation.
