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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.

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价格未确认★ 1,198 GitHub Stars目录更新于 · 2026年9月4日agent-skill

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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 adddess: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

Resources

For working code examples, see examples.md.

Status: Production Ready Last Updated: 2026-01-11 Maintained by: MoAI-ADK Backend Team

Common Rationalizations

Rationalization	Reality
"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)

Refactor Notes

Refactor scope (deferred to future sub-SPEC):

Narrow body to API design decision matrix (REST vs GraphQL vs tRPC vs gRPC)
Extract language-specific implementation details into Level-3 modules
Remove framework-specific content that duplicates language skill coverage

This skill is retained in v3.0 but its body will be restructured in a follow-up SPEC.

查看来源 ↗

文件元数据

name: moai-domain-backend
description: >
  Backend development specialist covering API design, database integration,
  microservices architecture, and modern backend patterns. Use when designing
  APIs, implementing server logic, authentication, or authorization.

when_to_use: >
  Use for backend work: API design (REST, GraphQL, gRPC), server logic,
  authentication and authorization, microservices, middleware, caching,
  and frameworks (FastAPI, Express, Django, Flask). Covers serverless,
  PostgreSQL, MongoDB, and Redis integration.

license: Apache-2.0
compatibility: Designed for Claude Code
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
user-invocable: false
metadata:
  version: "1.0.0"
  category: "domain"
  status: "active"
  updated: "2026-01-11"
  modularized: "false"
  tags: "backend, api, database, microservices, architecture"
  author: "MoAI-ADK Team"

查看原始文本

---
name: moai-domain-backend
description: >
  Backend development specialist covering API design, database integration,
  microservices architecture, and modern backend patterns. Use when designing
  APIs, implementing server logic, authentication, or authorization.

when_to_use: >
  Use for backend work: API design (REST, GraphQL, gRPC), server logic,
  authentication and authorization, microservices, middleware, caching,
  and frameworks (FastAPI, Express, Django, Flask). Covers serverless,
  PostgreSQL, MongoDB, and Redis integration.

license: Apache-2.0
compatibility: Designed for Claude Code
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
user-invocable: false
metadata:
  version: "1.0.0"
  category: "domain"
  status: "active"
  updated: "2026-01-11"
  modularized: "false"
  tags: "backend, api, database, microservices, architecture"
  author: "MoAI-ADK Team"
---

# 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 adddess: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

---

## Resources

For working code examples, see [examples.md](examples.md).

Status: Production Ready
Last Updated: 2026-01-11
Maintained by: MoAI-ADK Backend Team

<!-- moai:evolvable-start id="rationalizations" -->
## Common Rationalizations

| Rationalization | Reality |
|---|---|
| "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.

<!-- moai:evolvable-end -->

<!-- moai:evolvable-start id="red-flags" -->
## 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

<!-- moai:evolvable-end -->

<!-- moai:evolvable-start id="verification" -->
## 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)

<!-- moai:evolvable-end -->

## Refactor Notes

**Refactor scope** (deferred to future sub-SPEC):
- Narrow body to API design decision matrix (REST vs GraphQL vs tRPC vs gRPC)
- Extract language-specific implementation details into Level-3 modules
- Remove framework-specific content that duplicates language skill coverage

This skill is retained in v3.0 but its body will be restructured in a follow-up SPEC.

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许可证: Apache-2.0
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安装前审查: 避免自动安装

许可证: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
SKILL.md lacks an explicit 'Limitations' or 'Safety' section to define safe operating boundaries and potential risks.
The allowed-tools list is broad (Bash with many package managers, docker, curl) without guidance on when these are safe to use, which could lead to unintended side effects if not carefully scoped.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
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请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: modu-ai/moai-adk
许可证: Apache-2.0
版本: 1.0.0
最近 GitHub 推送: 2026年9月3日
目录更新于: 2026年9月4日
技能指令路径: .claude/skills/moai-domain-backend/SKILL.md @ 7ad9f8534dc4

版本来自目录元数据，使用前请核实来源发布记录。

质量

75/100

强

信任

58/100

Do not auto-install

审计

75/100

需审查

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Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
SKILL.md lacks an explicit 'Limitations' or 'Safety' section to define safe operating boundaries and potential risks.
The allowed-tools list is broad (Bash with many package managers, docker, curl) without guidance on when these are safe to use, which could lead to unintended side effects if not carefully scoped.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
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      "install": "npx skills add modu-ai/moai-adk --skill moai-domain-backend",
      "installSafety": "standard package or runtime install path",
      "permissionSurface": "secrets or environment access, shell or command execution",
      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "SKILL.md lacks an explicit 'Limitations' or 'Safety' section to define safe operating boundaries and potential risks.",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "SKILL.md lacks an explicit 'Limitations' or 'Safety' section to define safe operating boundaries and potential risks.",
      "The allowed-tools list is broad (Bash with many package managers, docker, curl) without guidance on when these are safe to use, which could lead to unintended side effects if not carefully scoped.",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 75,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "SKILL.md lacks an explicit 'Limitations' or 'Safety' section to define safe operating boundaries and potential risks.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "The allowed-tools list is broad (Bash with many package managers, docker, curl) without guidance on when these are safe to use, which could lead to unintended side effects if not carefully scoped."
  ],
  "agent_contract": {
    "task_input": "Use moai-domain-backend in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 66/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "modu-ai-moai-domain-backend (moai-domain-backend)",
      "install_command": "npx skills add modu-ai/moai-adk --skill moai-domain-backend",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "modu-ai-moai-domain-backend",
      "task": "Use moai-domain-backend in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/modu-ai-moai-domain-backend",
    "api": "https://www.openagentskill.com/api/agent/skills/modu-ai-moai-domain-backend",
    "audit": "https://www.openagentskill.com/skills/modu-ai-moai-domain-backend/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=modu-ai-moai-domain-backend&task=Use%20moai-domain-backend%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20moai-domain-backend%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20moai-domain-backend%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/modu-ai-moai-domain-backend/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/modu-ai-moai-domain-backend"
  }
}

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创作者: modu-ai
来源: modu-ai/moai-adk
收录方: OpenAgentSkill 社区索引

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