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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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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.

ソースを見る ↗

ファイルのメタデータ

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"

元のテキストを表示

---
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.

ソースを確認

価格と実行コスト

Skill の入手: 価格未確認
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ライセンス: Apache-2.0
価格未確認: 価格は未確認です。既存のソースとインストールリンクは利用できます。

無料で入手できても実行が無料とは限りません。価格は安全評価ではありません。価格情報を送る →

スキルのソースを記録済み

手順のパスを記録しています。実行テスト、安全保証、互換性認証ではありません。

インストール前にレビュー: 自動インストールを避ける

ライセンス: 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

完全な監査を開く

ツール一覧はメタデータであり、互換性のテスト結果ではありません。プロンプトは提案です。

小さなタスクから始める

1ソースを読み、入力、出力、依存関係、権限を確認します。
2Agent に計画を求め、設定と費用を承認してから隔離環境でテストします。
3出力と変更ファイルを確認し、実行した結果だけを報告します。再現用にソースの版を保存します。

依存関係、API キー、外部サービスの料金をソースで確認してください。公開リポジトリでも全サービスが無料とは限りません。

出典と利用上の注意

登録済み

メタデータと審査情報は参考です。人気、ソースの発見、実行成功は別の事実です。

ソースリポジトリ: modu-ai/moai-adk
ライセンス: Apache-2.0
バージョン: 1.0.0
最終 GitHub プッシュ: 2026年9月3日
登録情報の更新日: 2026年9月4日
手順のパス: .claude/skills/moai-domain-backend/SKILL.md @ 7ad9f8534dc4

登録されたバージョンです。ソースのリリース情報を確認してください。

品質

75/100

強い

信頼

58/100

Do not auto-install

監査

75/100

要レビュー

完全な監査を開く評価レポートを見る

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: —
成果: —

コピーはインストールではありません。件数は成功報告に基づき、品質全体を保証しません。

Agent 接続

Registry API 経由で判断、信頼、監査、ユースケース、インストールのシグナルを提供し、UI をスクレイピングせずに Agent が順位付けできます。

Install API を開く LLM テキスト形式 Manifest を開く自動解決プラン

詳細情報

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      "lastPushed": "1mo since push",
      "license": "Apache-2.0",
      "repository": "https://github.com/modu-ai/moai-adk/tree/main/.claude/skills/moai-domain-backend",
      "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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この掲載は公開ソースから登録されており、メンテナー申請が承認されるまで公式として表示されません。

作成者: modu-ai
ソース: modu-ai/moai-adk
インデックス作成者: OpenAgentSkill コミュニティインデックス

帰属は公開リポジトリまたは作成者プロフィールにリンクされています。作成者は掲載を申請して所有権シグナルを更新できます。

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この Registry により登録掲載は modu-ai に帰属していますが、まだ公式として表示されていません。申請すると、確認済み所有者シグナルが追加され、今後の公開、インストール、監査更新の信頼性が高まります。

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開発者がリポジトリを評価する場所で、正規掲載、現在の信頼・監査シグナル、実際の Agent-Proven エビデンスを表示します。

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/modu-ai-moai-domain-backend?metric=listed&label=Listed)](https://www.openagentskill.com/skills/modu-ai-moai-domain-backend?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/modu-ai-moai-domain-backend?metric=trust&label=Trust)](https://www.openagentskill.com/skills/modu-ai-moai-domain-backend?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/modu-ai-moai-domain-backend?metric=audit&label=Audit)](https://www.openagentskill.com/skills/modu-ai-moai-domain-backend/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/modu-ai-moai-domain-backend?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/modu-ai-moai-domain-backend?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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このスキルが Agent ワークフローに役立つかを共有してください。集約されたフィードバックがランキングを改善します。

概要