What problem does it solve? When a feature introduces new entities, teams often struggle to decide where domain boundaries lie, resulting in god-contexts, bloated aggregates, or over-modeled value objects. This Skill provides a structured method for decomposing a feature into bounded contexts and aggregates using Domain-Driven Design principles. ## Core Features & Use Cases - Strategic Context Discovery: Extracts candidate entities from user stories, groups them by language boundary, and analyzes lifecycles and consistency boundaries to produce a context map with relationship types (Shared Kernel, Customer-Supplier, Conformist, Anti-Corruption Layer). - Tactical Aggregate Design: Enforces aggregate rules such as one root per consistency boundary, cross-aggregate references by ID only, and keeping junction tables out of the domain model, with TypeScript/Zod code templates. - Split Heuristics and Anti-Patterns: Provides measurable thresholds for splitting contexts and worked examples of god-context and over-modeling traps, plus a bad-practices section covering god aggregates and direct cross-aggregate references. - Use Case: When planning a Kanban feature with Board and Card entities, use this Skill to determine they belong in two separate contexts due to different lifecycles and invariants, rather than one catch-all kanban context. ## Quick Start Ask the AI to apply the ddd-modeling skill to decompose your feature's user stories into bounded contexts and aggregate boundaries before implementation.