What problem does it solve? Writing efficient PostgreSQL queries and schemas requires knowing which index types, data types, and query patterns to use, and mistakes like missing indexes or wrong types cause slow queries and production issues. ## Core Features & Use Cases - Index Selection Guide: Cheat sheet mapping query patterns to the correct index type (B-tree, GIN, BRIN, composite, partial, covering). - Data Type & Schema Best Practices: Reference tables for choosing correct types like bigint IDs, timestamptz, and numeric for money. - Anti-Pattern Detection: Ready-to-run SQL queries that find unindexed foreign keys, slow queries via pg_stat_statements, and table bloat. - Use Case: When writing a migration for a new orders table, consult the patterns to create a composite index on (status, created_at), use timestamptz columns, and add an optimized RLS policy with the SELECT-wrapped auth.uid() pattern. ## Quick Start Ask the AI to review your PostgreSQL schema or slow query using the postgres-patterns skill and suggest proper indexes and data types.