What problem does it solve? Domain modeling often produces ambiguous boundaries: the same noun means different things in different contexts, or similar concepts get duplicated when they could share one model. This Skill resolves that ambiguity by systematically deciding whether concepts should be generalized, split, or kept specific. ## Core Features & Use Cases - Verb-first generalization: Groups concepts by shared actions and consumer effects rather than by noun similarity, producing reusable abstractions like availability or scheduling. - Meaning-based splitting: Splits a word into separate context-specific models when different contexts need different data, commands, or actors. - Interactive boundary confirmation: Asks one clarifying question at a time before finalizing any boundary decision, with counterexample attacks on every proposed generalization. - Use Case: After an event storming session for an e-commerce system, feed the concept list in and receive a distilled context map showing which concepts generalize (e.g., reservation across inventory and shipping) and which must split (e.g., 'order' in checkout vs. fulfillment). ## Quick Start Ask the assistant to distill the bounded contexts from your domain description or event storming output and confirm each proposed boundary through its questions.
