What problem does it solve? Designing a data layer for AI agent SaaS applications requires coordinating relational data, graph relationships, and caching across multiple databases, which is error-prone without proven patterns for schema design, query optimization, and cross-database consistency. ## Core Features & Use Cases - Multi-Database Patterns: Defines clear responsibilities for PostgreSQL (users, projects, threads), Neo4j (knowledge graphs, hierarchies), and Redis (sessions, LLM caching, rate limiting). - Implementation Templates: Provides Prisma schemas, Neo4j Cypher queries, and Upstash Redis helpers with connection pooling and type-safe caching. - Performance & Reliability: Covers N+1 query prevention, indexing strategies, migrations, backups, and cross-database transactional coordination. - Use Case: When building a chat-based AI agent platform, use this Skill to scaffold the Prisma schema, set up Neo4j for building hierarchy traversal, and add Redis-based LLM response caching with rate limiting. ## Quick Start Ask the AI to design a multi-database architecture with PostgreSQL, Neo4j, and Redis for your AI agent SaaS application.