KCAE.Profiles:2.2 - What the cookbooks contribute, and what must change

The skill-suggestion cookbook supplies progressive selection: short descriptions for a wide ranking, fuller descriptions plus instruction openings for the shortlisted Choice, and full descriptions for per-candidate fits. Its Jev 1.12 demonstration uses synthetic requests. The inspected code gates on the maximum fits score, then returns the Choice winner; these can refer to different candidates. This is a static example defect, not a measured failure rate or a general vendor claim. KCAE.ASSESS:4.4 binds adequacy to the returned candidate. Method-library use also replaces the example’s action-oriented screen with the required contribution, including explanations, and reads complete necessary instructions before reliance.

The feature-discovery cookbook proposes natural-language questions, obtains numeric features, uses supervised-model errors to revise them and assesses held-out data. It demonstrates this on a bounded tasting-note prediction problem. KCAE.MEMORY adapts the error-to-question loop for recognition aids, while requiring actual permitted episodes and a separate test population. The adaptation is a proposed domain construction, not evidence that the cookbook already solves open-ended method noticing.