MoAI-ADK is a Go-based harness that organizes and verifies Claude Code work across planning, implementation, synchronization, and review stages. Developers use it to structure agentic coding tasks, apply quality gates, and route work across language models, while the catalogue entries extend its workflow with skills, hooks, commands, MCP servers, instructions, and settings.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add modu-ai/moai-adk --skill moai-domain-backendgit clone --depth 1 https://github.com/modu-ai/moai-adkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/modu-ai/moai-adk/moai-domain-backend)<a href="https://agentmods.dev/skills/modu-ai/moai-adk/moai-domain-backend"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-adk/moai-domain-backend/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/modu-ai/moai-adk/moai-domain-backend"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-adk/moai-domain-backend.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.01753 |
| Opus 5.5 | $0.00016 | $0.00701 |
| Sonnet 5.5 | $0.00008 | $0.00351 |
| Haiku 4.5 | $0.00004 | $0.00175 |
Grade A, and why
moai-domain-backend scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 22d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
allowed-tools: Read, Write, Edit, Bash(npm:*), Bash(npx:*), Bash(node:*), Bash(uv:*), Bash(pip:*), Bash(pytest:*), Bash(ruff:*), Bash(docker:*), Bash(curl:*), Bash(go:*), Bash(cargo:*), Grep, Glob How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 22d ago Changed · -14 lines fdff72ab6572
- 1mo ago First seen · 197 lines · 39 tokens per session scan A 259d3f799429
moai-domain-backend is a skill published in the GitHub repository modu-ai/moai-adk (1,243 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 1,753 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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