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Moai Domain Backend

OrganizationPopular
modu-ai
moai-domain-backend

Backend development specialist covering API design, database integration, microservices architecture, and modern backend patterns. Use when designing APIs, implementing server logic, authentication, or authorization.

Overview

Publishermodu-ai
Repositorymoai-adk
Skill namemoai-domain-backend
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 Backend 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-backend
  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-backend .claude/skills/moai-domain-backend
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Moai Domain Backend 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 Backend 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 Backend 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.

Backend Development Specialist

Quick Reference

Backend Development Mastery - Comprehensive backend development patterns covering API design, database integration, microservices, and modern architecture patterns.

Core Capabilities:

  • API Design: REST, GraphQL, gRPC with OpenAPI 3.1
  • Database Integration: PostgreSQL, MongoDB, Redis, caching strategies
  • Microservices: Service mesh, distributed patterns, event-driven architecture
  • Security: Authentication, authorization, OWASP compliance
  • Performance: Caching, optimization, monitoring, scaling

When to Use:

  • Backend API development and architecture
  • Database design and optimization
  • Microservices implementation
  • Performance optimization and scaling
  • Security integration for backend systems

Implementation Guide

API Design Patterns

RESTful API Architecture:

Create a FastAPI application with authentication and response models. Define a Pydantic UserResponse model with id, email, and name fields. Implement list_users and create_user endpoints with HTTPBearer security dependency. The list endpoint returns a list of UserResponse objects, while the create endpoint accepts a UserCreate model and returns a single UserResponse.

GraphQL Implementation:

Use Strawberry to define GraphQL types. Create a User type with id, email, and name fields. Define a Query type with a users resolver that returns a list of User objects asynchronously. Generate the schema by passing the Query type to strawberry.Schema.

Database Integration Patterns

PostgreSQL with SQLAlchemy:

Define SQLAlchemy models using declarative_base. Create a User model with id as primary key, email as unique string, and name as string column. Configure the engine with connection pooling parameters including pool_size of 20, max_overflow of 30, and pool_pre_ping enabled for connection health checks.

MongoDB with Motor:

Create a UserService class that initializes with an AsyncIOMotorClient. Set up the database and users collection in the constructor. Create indexes for email (unique) and created_at fields. Implement create_user method that inserts a document and returns the inserted_id as string.

Microservices Architecture

Service Discovery with Consul:

Create a ServiceRegistry class that connects to Consul. Implement register_service method that registers a service with name, id, port, and health check endpoint. Implement discover_service method that queries healthy services and returns list of address:port strings.

Event-Driven Architecture:

Create an EventBus class using aio_pika for AMQP messaging. Implement connect method to establish connection and channel. Implement publish_event method that serializes event type and data as JSON and publishes to the default exchange with routing_key matching the event type.


Advanced Patterns

Caching Strategies

Redis Integration:

Create a CacheManager class with Redis connection. Implement a cache_result decorator that accepts ttl parameter. The decorator generates cache keys from function name and arguments, checks Redis for cached results, executes the function on cache miss, and stores results with expiration. Use json.loads and json.dumps for serialization.

Security Implementation

JWT Authentication:

Create a SecurityManager class with CryptContext for bcrypt password hashing. Implement hash_password and verify_password methods using the context. Implement create_access_token that encodes a JWT with expiration time using HS256 algorithm. Default expiration is 15 minutes if not specified.

Performance Optimization

Database Connection Pooling:

Create an optimized SQLAlchemy engine with QueuePool, pool_size 20, max_overflow 30, pool_pre_ping enabled, and pool_recycle of 3600 seconds. Add event listeners for before_cursor_execute and after_cursor_execute to track query timing. Log warnings for queries exceeding 100ms threshold.


Works Well With

  • moai-domain-frontend - Full-stack development integration
  • moai-domain-database - Advanced database patterns
  • moai-foundation-core - MCP server development patterns for backend services
  • moai-foundation-quality + moai-ref-owasp-checklist - Security validation and compliance
  • moai-foundation-core - Core architectural principles

Technology Stack

Primary Technologies:

  • Languages: Python 3.13+, Node.js 20+, Go 1.23
  • Frameworks: FastAPI, Django, Express.js, Gin
  • Databases: PostgreSQL 16+, MongoDB 7+, Redis 7+
  • Message Queues: RabbitMQ, Apache Kafka, Redis Pub/Sub
  • Containerization: Docker, Kubernetes
  • Monitoring: Prometheus, Grafana, OpenTelemetry

Integration Patterns:

  • RESTful APIs with OpenAPI 3.1
  • GraphQL with Apollo Federation
  • gRPC for high-performance services
  • Event-driven architecture with CQRS
  • API Gateway patterns
  • Circuit breakers and resilience patterns

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

Common Rationalizations

RationalizationReality
"Input validation can happen on the frontend"Frontend validation is UX. Backend validation is security. They serve different purposes and both are required.
"This endpoint is internal, it does not need authentication"Internal endpoints are reachable from compromised services. Zero-trust means every endpoint validates identity.
"I will add error handling later"Unhandled errors leak stack traces, connection strings, and internal state. Error handling is day-one work.
"The ORM handles SQL injection"ORMs protect parameterized queries. Raw queries, dynamic filters, and ORDER BY clauses still need escaping.
"This API is backward-compatible, no version bump needed"Removing optional fields, changing defaults, or altering error formats are breaking changes to existing callers.
"Microservices are overkill, I will just add another endpoint"Unbounded endpoint growth creates a monolith-in-disguise. Evaluate service boundaries before adding.

Hyrum's Law: With a sufficient number of users, every observable behavior of your API will be depended on by somebody. Changing anything, no matter how trivial, can break someone.

Red Flags

  • API endpoint accepts user input without validation or sanitization
  • Error response includes stack trace or internal path information
  • Database credentials hardcoded in source code instead of environment variables
  • No rate limiting configured on public-facing endpoints
  • API returns 200 OK for every failure case with error in the response body
  • Raw SQL queries built with string concatenation

Verification

  • Every endpoint validates input before processing (show validation middleware or checks)
  • Error responses use standard format without internal details (show sample error response)
  • Credentials sourced from environment variables, not config files (grep for hardcoded secrets)
  • Rate limiting configured on public endpoints (show middleware registration)
  • API versioning strategy documented and enforced
  • Database queries use parameterized statements (no string concatenation with user input)
  • Authentication required on all non-public endpoints (show auth middleware)

Frequently asked questions

What does the Moai Domain Backend AI skill do?

Backend development specialist covering API design, database integration, microservices architecture, and modern backend patterns. Use when designing APIs, implementing server logic, authentication, or authorization.

Why use Moai Domain Backend on TypingMind?

Because you install it once and use it with any model. Moai Domain Backend 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 Backend 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-backend. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Moai Domain Backend?

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 Backend?

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

Is the Moai Domain Backend 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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