Database Architecture

Designs multi-database architectures combining PostgreSQL, Neo4j, and Redis for AI agent SaaS applications.

Updated Apr 2, 2026
One-click install
npx skills add https://github.com/khiwniti/carbonscope --skill database-architecture-khiwniti
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Database Architecture
Source: https://github.com/khiwniti/carbonscope/tree/main/ai-agent-saas-expert/skills/database-architecture
Command: npx skills add https://github.com/khiwniti/carbonscope --skill database-architecture-khiwniti

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about Database Architecture

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a multi-database architecture for an AI agent SaaS?▼

Assign each database a clear role: PostgreSQL for users, projects, and threads; Neo4j for knowledge graphs and hierarchies; Redis for sessions, LLM caching, and rate limits. Coordinate writes across databases with explicit cross-database operation functions.

How to avoid N+1 query problems with Prisma?▼

Use Prisma's include option to fetch related records in one query, or select to retrieve only needed fields. This replaces looping over results and issuing a separate query per record, which causes the N+1 problem.

Should I use Neo4j or PostgreSQL for hierarchical data?▼

Use Neo4j for deep hierarchies, path queries, and relationship traversals like BIM building structures, since Cypher handles variable-length paths natively. PostgreSQL fits transactional relational data such as users, projects, and threads.

How do I cache LLM responses with Redis?▼

Hash the prompt with SHA-256 to build a cache key, check Redis before calling the model, and store the response with a TTL such as one hour. Upstash Redis provides a simple get/set interface for this pattern.

How do I keep data consistent across PostgreSQL and Neo4j?▼

Perform operations in a defined order within a single function: write to PostgreSQL first, then Neo4j, then update the cache. On failure, catch errors and apply rollback or cleanup logic to avoid orphaned records.