Fine-tune Kev on the user's workload

Jev (TypeSafe's System One model) answers typed questions about a text without generating tokens, but it is a fixed hosted model: on the user's data it is out of distribution and its probabilities cannot be recalibrated. Kev is the open reconstruction; because it can be trained, you can fine-tune it on a few hundred to a few thousand labelled examples of the user's exact questions and fit its temperature on a held-out slice, so the probabilities it serves are calibrated for their data. This skill takes a user from "I have this decision to automate" to a deployed, measured endpoint, then cleans up. Nothing needs a local GPU or a clone of the Kev repo.

Scripts (scripts/) are short, standard-library Python plus one Modal app. If the user's case does not fit them, read the script and change it; they are meant to be edited, not worked around.