What problem does it solve? Designing database schemas without a systematic approach leads to redundant data, slow queries, and painful migrations. This Skill provides concrete patterns for ER modeling, normalization, indexing, partitioning, and schema evolution across Postgres, MySQL, MongoDB, and multi-database architectures. ## Core Features & Use Cases - ER Modeling & Normalization: Design one-to-many, many-to-many, and self-referencing relationships, then normalize to 3NF/BCNF with worked SQL examples. - Index & Query Optimization: Apply B-tree, partial, composite, GIN, and JSONB index strategies, plus monitoring queries to find unused or missing indexes. - Schema Evolution & Scaling: Execute zero-downtime migrations with the expand-migrate-contract pattern, partition large tables by range or list, and implement soft deletes with audit trails. - Use Case: When building an order management system, use this Skill to model users and orders, add composite indexes for common queries, cache aggregates via triggers, and plan a safe migration path as the schema evolves. ## Quick Start Ask the AI to design a normalized Postgres schema for your application's entities, including indexes and a migration plan.