moai-domain-database

name	moai-domain-database
description	Database specialist covering PostgreSQL, MongoDB, Redis, Oracle, and cloud database platforms (Neon, Supabase, Firestore). Use for schema design, query optimization, indexing strategies, data modeling, or cloud database selection. Cloud vendor guide absorbed from moai-platform-database-cloud.
when_to_use	Use for schema design, query optimization, indexing strategies, and ORMs/ODMs (Prisma, Mongoose, SQLAlchemy, Drizzle). Covers PostgreSQL, MongoDB, Redis, Oracle, and cloud databases (Neon, Supabase, Firestore).
license	Apache-2.0
compatibility	Designed for Claude Code
allowed-tools	Read, Write, Edit, Bash(psql:*), Bash(mysql:*), Bash(sqlite3:*), Bash(mongosh:*), Bash(redis-cli:*), Bash(npm:*), Bash(npx:*), Bash(prisma:*), Bash(neonctl:*), Bash(firebase:*), Bash(supabase:*), Grep, Glob
user-invocable	false
metadata	{"version":"2.0.0","category":"domain","status":"active","updated":"2026-04-25","tags":"database, postgresql, mongodb, redis, oracle, data-patterns, performance, neon, supabase, firestore, cloud-database, serverless","author":"MoAI-ADK Team","related-skills":"moai-platform-database-cloud"}
progressive_disclosure	{"enabled":true,"level1_tokens":100,"level2_tokens":5000}

Database Domain Specialist

Quick Reference

Enterprise Database Expertise - Comprehensive database patterns and implementations covering PostgreSQL, MongoDB, Redis, Oracle, and advanced data management for scalable modern applications.

Core Capabilities:

• PostgreSQL: Advanced relational patterns, optimization, and scaling
• MongoDB: Document modeling, aggregation, and NoSQL performance tuning
• Redis: In-memory caching, real-time analytics, and distributed systems
• Oracle: Enterprise patterns, PL/SQL, partitioning, and hierarchical queries
• Multi-Database: Hybrid architectures and data integration patterns
• Performance: Query optimization, indexing strategies, and scaling
• Operations: Connection management, migrations, and monitoring

When to Use:

• Designing database schemas and data models
• Implementing caching strategies and performance optimization
• Building scalable data architectures
• Working with multi-database systems
• Optimizing database queries and performance

---

Implementation Guide

Quick Start Workflow

Database Stack Initialization:

Create a DatabaseManager instance and configure multiple database connections. Set up PostgreSQL with connection string, pool size of 20, and query logging enabled. Configure MongoDB with connection string, database name, and sharding enabled. Configure Redis with connection string, max connections of 50, and clustering enabled. Use the unified interface to query user data with profile and analytics across all database types.

Single Database Operations:

Run PostgreSQL schema migrations using the migration command with the database type and migration file path. Execute MongoDB aggregation pipelines by specifying the collection name and pipeline JSON file. Warm Redis cache by specifying key patterns and TTL values.

Core Components

PostgreSQL Module:

• Advanced schema design and constraints
• Complex query optimization and indexing
• Window functions and CTEs
• Partitioning and materialized views
• Connection pooling and performance tuning

MongoDB Module:

• Document modeling and schema design
• Aggregation pipelines for analytics
• Indexing strategies and performance
• Sharding and scaling patterns
• Data consistency and validation

Redis Module:

• Multi-layer caching strategies
• Real-time analytics and counting
• Distributed locking and coordination