What problem does it solve? Python domain models often accumulate ad-hoc methods: fetch-and-return wrappers, multi-step construction chains, stored copies of derivable facts, and free functions computing from model fields. This Skill enforces a closed set of two behavior constructs — the verb (state transition on the consistency model) and the derivation (pure computed fact on a frozen value) — so every method has exactly one legal form. ## Core Features & Use Cases - Verb construct: Defines state-transition methods with at most one construction statement, optional foreign-reply capture, state-field assignment, and emission of the constructed fact through a client field. - Derivation construct: Defines pure functions of a frozen model's fields as @property, @cached_property, or @computed_field, with a closed computation algebra (arithmetic, folds, extrema, selection, case-table lookup). - Query models: Answers questions with inputs via frozen composite models whose derivation returns a constructed union such as PriceFound | PriceMissing. - Use Case: When writing a trading domain model, use this Skill to decide whether exposure belongs as a @cached_property derivation on Fill, and to write book() as a verb that constructs Position in one statement and publishes it. ## Quick Start Ask the AI to write a state-transition method or computed property on your Pydantic model following the verb and derivation rules.
