Tacit knowledge is a type of knowledge often existing in one's subconscious or embodied in muscle memory. Such knowledge is pervasive in creative practices yet remains difficult to observe or codify. To better understand tacit knowledge, we introduce a design method that leverages time-series data (interaction logs, physical sensor, and biosignal data) to isolate unique actions and behaviors between groups of users. This method is enacted in Eluent, a tool that distills hundreds of hours of dense activity data using an activity segmentation algorithm into a codebook – a set of distinct, characteristic sequences that comprise an activity. The results are made visually parsable in a representation we term process chromatograms that aid with 1) highlighting distinct periods of activity in creative sessions, 2) identifying distinct groups of users, and 3) characterizing periods of activity. We demonstrate the value of our method through a study of tacit process within computational notebooks and discuss ways process chromatograms can act as a knowledge mining technique, an evaluation metric, and a design-informing visualization.