What problem does it solve? Choosing the wrong data representation leads to lockstep mutations, repeated conversions, defensive checks, and hidden performance costs. This Skill guides you through analyzing actual operations, scale, relationships, and invariants before committing to a data structure, so the representation fits the workload instead of fighting it. ## Core Features & Use Cases - Workload-Driven Selection: Analyze operation frequency, cardinality, ownership, concurrency, and persistence requirements before comparing candidate representations. - Invariant and Source-of-Truth Modeling: Define uniqueness, ordering, and referential integrity rules, with explicit update or invalidation rules for every index and cache. - Warning-Sign Detection: Identify representations that cause synchronized collections, repeated regrouping, or scattered defensive checks, and know when to fix them. - Use Case: When designing a new core entity model, use this Skill to compare an array of structs against a keyed map with secondary indexes, record the dominant operations and invariants, and verify the choice with boundary and mutation tests. ## Quick Start Ask the AI to review the data structures in your core state module and recommend a representation based on the actual operations, scale, and invariants.
