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Moai Domain Database

OrganizationPopular
modu-ai
moai-domain-database

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.

Overview

Publishermodu-ai
Repositorymoai-adk
Skill namemoai-domain-database
Stars
1.2K
Forks
226
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by modu-ai on GitHub. Read the source before you install it.

Installation

Install the Moai Domain Database AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/modu-ai/moai-adk/tree/main/.claude/skills/moai-domain-database
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/modu-ai/moai-adk.git /tmp/moai-adk
mkdir -p .claude/skills
cp -r /tmp/moai-adk/.claude/skills/moai-domain-database .claude/skills/moai-domain-database
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Moai Domain Database in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Moai Domain Database on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Moai Domain Database is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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
  • Pub/sub messaging and streams
  • Advanced data structures including HyperLogLog and Geo

Oracle Module:

  • Hierarchical and recursive query patterns (CONNECT BY)
  • PL/SQL procedures, packages, and batch operations
  • Partitioning strategies (range, list, hash, composite)
  • Enterprise features and statement caching
  • LOB handling and large data processing

Advanced Patterns

Multi-Database Architecture

Polyglot Persistence Pattern:

Create a DataRouter class that initializes connections to PostgreSQL, MongoDB, Redis, and Oracle. Implement get_user_profile method that retrieves structured user data from PostgreSQL or Oracle, flexible profile data from MongoDB, and real-time status from Redis, then merges all data sources. Implement update_user_data method that routes structured data updates to PostgreSQL/Oracle, profile data updates to MongoDB, and real-time data updates to Redis, followed by cache invalidation.

Data Synchronization:

Create a DataSyncManager class that synchronizes user data across databases. Implement sync_user_data method that retrieves user from PostgreSQL, creates a search document for MongoDB, upserts to the MongoDB search collection, creates cache data, and updates Redis cache with TTL.

Performance Optimization

Query Performance Analysis:

For PostgreSQL, execute EXPLAIN ANALYZE BUFFERS on queries and use a QueryAnalyzer to generate optimization suggestions. For MongoDB, create an AggregationOptimizer to analyze and optimize aggregation pipelines. For Redis, retrieve info metrics and use a PerformanceAnalyzer to generate recommendations.

Scaling Strategies:

Configure PostgreSQL read replicas by providing replica connection URLs. Set up MongoDB sharding with shard key and number of shards. Configure Redis clustering by providing node URLs for the cluster.


Works Well With

Complementary Skills:

  • moai-domain-backend - API integration and business logic
  • moai-foundation-core - Database migration and schema management
  • moai-workflow-project - Database project setup and configuration
  • moai-platform-supabase - Supabase database integration patterns
  • moai-platform-neon - Neon database integration patterns
  • moai-platform-firestore - Firestore database integration patterns

Technology Integration:

  • ORMs and ODMs including SQLAlchemy, Mongoose, and TypeORM
  • Connection pooling with PgBouncer and connection pools
  • Migration tools including Alembic, Flyway, and Data Pump
  • Monitoring with pg_stat_statements, MongoDB Atlas, and Oracle AWR
  • python-oracledb for Oracle connectivity and PL/SQL execution
  • Cache invalidation and synchronization

Technology Stack

Relational Database:

  • PostgreSQL 14+ as primary database
  • MySQL 8.0+ as alternative
  • Connection pooling with PgBouncer and SQLAlchemy

NoSQL Database:

  • MongoDB 6.0+ as primary document store
  • Document modeling and validation
  • Aggregation framework
  • Sharding and replication

In-Memory Database:

  • Redis 7.0+ as primary cache
  • Redis Stack for advanced features
  • Clustering and high availability
  • Advanced data structures

Enterprise Database:

  • Oracle 19c+ / 21c+ for enterprise workloads
  • python-oracledb (successor to cx_Oracle)
  • PL/SQL procedures and packages
  • Partitioning and advanced analytics

Supporting Tools:

  • Migration tools including Alembic and Flyway
  • Monitoring with Prometheus and Grafana
  • ORMs and ODMs including SQLAlchemy and Mongoose
  • Connection management utilities

Performance Features:

  • Query optimization and analysis
  • Index management and strategies
  • Caching layers and invalidation
  • Load balancing and failover

Resources

Status: Production Ready Maintained by: MoAI-ADK Database Team

Common Rationalizations

RationalizationReality
"I do not need an index, the table is small"Tables grow. The missing index that is invisible at 1K rows becomes a production incident at 1M rows.
"I will add the migration later"Schema changes without migrations are unreproducible. Every change must have a reversible migration script.
"This query works fine in development"Development databases have tiny datasets. Production query plans differ dramatically at scale. Explain analyze first.
"NoSQL does not need schema design"Schemaless does not mean designless. Document structure decisions affect every query and index.
"I will just add a column, it is non-breaking"Adding a NOT NULL column without a default breaks existing inserts. Column additions need default values or migration backfills.
"Connection pooling is handled by the framework"Framework defaults are generic. Pool size, timeout, and idle limits must be tuned to the workload.

Red Flags

  • Schema change committed without a corresponding migration file
  • Query uses SELECT * in production code instead of explicit column list
  • No index exists for columns used in WHERE, JOIN, or ORDER BY clauses
  • Connection string hardcoded in source instead of environment variable
  • Transaction scope spans user-facing HTTP request duration (long-held locks)
  • No EXPLAIN ANALYZE output for new queries touching large tables

Verification

  • Migration file exists for every schema change (show migration file list)
  • Indexes exist for frequently queried columns (show index definitions)
  • EXPLAIN ANALYZE run for new queries on representative data (show output)
  • Connection credentials sourced from environment variables
  • Transaction scopes are minimal and do not span I/O waits
  • Backup and restore procedure documented and tested
  • Connection pool settings configured with explicit size and timeout

Cloud Vendor Guide (absorbed from moai-platform-database-cloud)

Cloud database platform selection and configuration for Neon, Supabase, and Firebase Firestore.

Quick Decision Guide

NeedPlatform
Serverless PostgreSQL with auto-scalingNeon
Database branching for CI/CD previewsNeon branching
Edge-compatible connection poolingNeon + Neon Proxy
Vector search (pgvector) for AI/MLSupabase
Row-Level Security for multi-tenant appsSupabase RLS
Real-time subscriptions + full-stackSupabase
Mobile-first with offline syncFirebase Firestore
Cross-platform (iOS/Android/Web)Firebase Firestore

Neon (Serverless PostgreSQL)

Key features: Auto-scaling compute, scale-to-zero, database branching, pg_bouncer pooling.

Setup:

bash
npm install @neondatabase/serverless
# Connection string: postgresql://user:pass@ep-xxx.neon.tech/dbname?sslmode=require

Branch workflow: Create a branch per PR (neonctl branches create --name pr-123), run migrations, test, delete on merge. Zero cost during idle periods.

Supabase (PostgreSQL 16)

Key features: pgvector, Row-Level Security, real-time subscriptions, built-in auth/storage.

RLS policy pattern:

sql
CREATE POLICY "users_own_data" ON items
  FOR ALL USING (auth.uid() = user_id);

pgvector search: SELECT * FROM embeddings ORDER BY embedding <-> $1 LIMIT 10;

Firebase Firestore (NoSQL)

Key features: Real-time sync, offline caching, Security Rules, mobile SDKs.

Security Rules pattern:

javascript
match /users/{userId} {
  allow read, write: if request.auth.uid == userId;
}

Offline persistence: Enable via enableIndexedDbPersistence(db) (web) or SDK default (mobile).

Frequently asked questions

What does the Moai Domain Database AI skill do?

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.

Why use Moai Domain Database on TypingMind?

Because you install it once and use it with any model. Moai Domain Database is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Moai Domain Database in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/modu-ai/moai-adk/tree/main/.claude/skills/moai-domain-database. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Moai Domain Database?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Moai Domain Database?

As many as you like. As long as a model supports skills, you can use Moai Domain Database with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Moai Domain Database AI skill free?

Yes. It is published on GitHub by modu-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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