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Rnd Fullstack Architect

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chendongqi
rnd-fullstack-architect

全栈架构设计专家 - 精通前后端架构设计、微服务架构、云原生架构、移动端架构。适用场景:(1) SaaS 产品架构设计,(2) 微服务拆分与治理,(3) 前端工程架构,(4) 移动端架构(iOS/Android/跨平台),(5) API 设计与网关架构,(6) 数据库选型与设计,(7) DevOps 与部署架构,(8) 高可用与容灾设计,(9) 性能优化与扩展策略

Overview

Publisherchendongqi
RepositoryOPB-Skills
Skill namernd-fullstack-architect
Stars
133
Forks
25
Bundled files
Instructions only
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 chendongqi on GitHub. Read the source before you install it.

Installation

Install the Rnd Fullstack Architect 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/chendongqi/OPB-Skills/tree/main/skills/rnd-fullstack-architect
  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/chendongqi/OPB-Skills.git /tmp/OPB-Skills
mkdir -p .claude/skills
cp -r /tmp/OPB-Skills/skills/rnd-fullstack-architect .claude/skills/rnd-fullstack-architect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rnd Fullstack Architect 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 Rnd Fullstack Architect 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 Rnd Fullstack Architect 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.

全栈架构设计师

系统架构全景

┌──────────────────────────────────────────────────────────┐
│                      客户端层                             │
│  ┌────────┐  ┌──────────┐  ┌────────┐  ┌────────┐       │
│  │  Web   │  │  Mobile  │  │Desktop │  │  IoT   │       │
│  │(React) │  │(RN/Flutter)│(Electron)│(Embedded)│       │
│  └────────┘  └──────────┘  └────────┘  └────────┘       │
├──────────────────────────────────────────────────────────┤
│                      接入层                               │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐      │
│  │     CDN     │  │   API网关   │  │     WAF     │      │
│  │(CloudFlare) │  │(Kong/Apisix)│  │  (防火墙)   │      │
│  └─────────────┘  └─────────────┘  └─────────────┘      │
├──────────────────────────────────────────────────────────┤
│                      服务层                               │
│  ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐│
│  │用户服务│ │订单服务│ │支付服务│ │消息服务│ │搜索服务││
│  └────────┘ └────────┘ └────────┘ └────────┘ └────────┘│
├──────────────────────────────────────────────────────────┤
│                      数据层                               │
│  ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐│
│  │ MySQL  │ │ Redis  │ │   ES   │ │ Kafka  │ │  OSS   ││
│  └────────┘ └────────┘ └────────┘ └────────┘ └────────┘│
└──────────────────────────────────────────────────────────┘

前端架构模式

分层架构 (Feature-Sliced Design)

src/
├── app/                    # 应用层:路由、布局、全局配置
│   ├── (routes)/
│   ├── layout.tsx
│   └── providers.tsx
├── features/               # 特性层:按业务模块组织
│   ├── auth/
│   │   ├── components/
│   │   ├── hooks/
│   │   ├── api/
│   │   └── types.ts
│   ├── dashboard/
│   └── settings/
├── shared/                 # 共享层
│   ├── components/         # 通用UI组件
│   ├── hooks/              # 通用Hooks
│   ├── utils/              # 工具函数
│   └── types/              # 类型定义
└── infrastructure/         # 基础设施层
    ├── api/                # API客户端 (axios/fetch)
    ├── store/              # 状态管理
    └── i18n/               # 国际化

状态管理选型

场景推荐方案特点
服务端状态TanStack Query / SWR缓存、后台刷新、乐观更新
客户端全局状态Zustand / Jotai轻量、原子化
表单状态React Hook Form性能优化、校验集成
URL状态nuqs / searchParams可分享、SEO友好

性能优化策略

1. 代码分割: React.lazy + Suspense
2. 虚拟列表: @tanstack/react-virtual
3. 图片优化: next/image, lazy loading
4. 缓存策略: Service Worker, HTTP Cache
5. 预加载: prefetch, preload, preconnect

后端架构模式

微服务拆分原则

1. 单一职责: 每个服务只做一件事
2. 业务边界: 按领域边界划分 (DDD)
3. 数据自治: 每个服务独立数据库
4. 独立部署: 服务可独立发布
5. 容错设计: 服务降级、熔断、限流

服务通信模式

模式场景技术选型
同步调用实时查询gRPC (内部) / REST (对外)
异步消息事件驱动Kafka / RabbitMQ
服务发现动态寻址Consul / Nacos / K8s Service
配置中心统一配置Apollo / Nacos / Consul

典型服务结构 (Clean Architecture)

service/
├── cmd/                    # 入口点
│   └── main.go
├── internal/
│   ├── domain/             # 领域层
│   │   ├── entity/         # 实体
│   │   ├── valueobject/    # 值对象
│   │   └── service/        # 领域服务
│   ├── application/        # 应用层
│   │   ├── usecase/        # 用例
│   │   └── dto/            # 数据传输对象
│   ├── infrastructure/     # 基础设施层
│   │   ├── repository/     # 仓储实现
│   │   └── external/       # 外部服务
│   └── interfaces/         # 接口层
│       ├── http/           # HTTP Handler
│       └── grpc/           # gRPC Handler
├── pkg/                    # 可导出包
└── api/                    # API定义
    ├── proto/              # gRPC定义
    └── openapi/            # OpenAPI定义

数据库架构

选型指南

类型场景推荐特点
关系型事务、复杂查询PostgreSQLACID、扩展性强
文档型灵活SchemaMongoDB水平扩展、聚合查询
缓存高频读取Redis毫秒响应、数据结构丰富
搜索全文检索Elasticsearch倒排索引、聚合分析
时序监控/IoTClickHouse/TimescaleDB高写入、压缩存储
图数据库关系网络Neo4j多跳查询、路径分析

分库分表策略

垂直拆分: 按业务模块分库 (用户库、订单库)
水平拆分: 按范围/哈希分表 (ShardingSphere)
读写分离: 主从复制 + 代理 (ProxySQL)

API 设计规范

RESTful 设计

GET    /api/v1/users              # 列表
POST   /api/v1/users              # 创建
GET    /api/v1/users/:id          # 详情
PUT    /api/v1/users/:id          # 全量更新
PATCH  /api/v1/users/:id          # 部分更新
DELETE /api/v1/users/:id          # 删除

# 关联资源
GET    /api/v1/users/:id/orders   # 用户订单列表

# 批量操作
POST   /api/v1/users/batch        # 批量创建
DELETE /api/v1/users/batch        # 批量删除

响应格式

json
{
  "code": 0,
  "message": "success",
  "data": { ... },
  "meta": {
    "page": 1,
    "per_page": 20,
    "total": 100,
    "request_id": "abc123"
  }
}

错误响应

json
{
  "code": 40001,
  "message": "Validation failed",
  "errors": [
    { "field": "email", "message": "Invalid email format" }
  ]
}

云原生部署架构

K8s 部署结构

yaml
Ingress (Nginx/Traefik)
    │
    ├── Frontend (Deployment + HPA)
    │       ├── ConfigMap (环境配置)
    │       └── Service (ClusterIP)
    │
    ├── Backend Services (Deployment + HPA)
    │       ├── Secrets (敏感配置)
    │       ├── Service (ClusterIP)
    │       └── PodDisruptionBudget
    │
    ├── StatefulSet (数据库/中间件)
    │       ├── PVC (持久卷)
    │       └── Headless Service
    │
    └── CronJob (定时任务)

高可用设计

多副本: Deployment replicas >= 2
多可用区: Pod Anti-Affinity
健康检查: Liveness + Readiness Probe
自动扩缩: HPA (CPU/Memory/Custom Metrics)
熔断降级: Istio / Sentinel

监控告警体系

┌─────────────────────────────────────────┐
│  日志: ELK / Loki                        │
│  指标: Prometheus + Grafana             │
│  链路: Jaeger / SkyWalking              │
│  告警: AlertManager / PagerDuty         │
└─────────────────────────────────────────┘

输出规范

架构设计文档应包含:

  • C4模型图(Context/Container/Component/Code)
  • 技术选型决策记录 (ADR)
  • API设计文档 (OpenAPI 3.0)
  • 数据库ER图
  • 部署架构图
  • 容量规划与扩展策略
  • 监控告警方案
  • 安全设计(认证/授权/加密/审计)
  • 灾备方案(RTO/RPO)

Frequently asked questions

What does the Rnd Fullstack Architect AI skill do?

全栈架构设计专家 - 精通前后端架构设计、微服务架构、云原生架构、移动端架构。适用场景:(1) SaaS 产品架构设计,(2) 微服务拆分与治理,(3) 前端工程架构,(4) 移动端架构(iOS/Android/跨平台),(5) API 设计与网关架构,(6) 数据库选型与设计,(7) DevOps 与部署架构,(8) 高可用与容灾设计,(9) 性能优化与扩展策略

Why use Rnd Fullstack Architect on TypingMind?

Because you install it once and use it with any model. Rnd Fullstack Architect 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 Rnd Fullstack Architect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chendongqi/OPB-Skills/tree/main/skills/rnd-fullstack-architect. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Rnd Fullstack Architect?

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 Rnd Fullstack Architect?

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

Is the Rnd Fullstack Architect AI skill free?

It is published on GitHub by chendongqi. Check the repository for licensing terms. 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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