System Design

Community

kkunkunya

system-design

系统架构决策与设计文档：Design Doc（含 Alternatives Considered 强制段）、ADR、结构影响分析。用于多模块、存量系统改造、共享边界、高风险集成、重大技术选型。不管前端技术栈和视觉表达（那是 frontend-design-research 的事）。

Overview

Publisher	kkunkunya
Repository	`ai-frontend-design-kit`
Skill name	`system-design`
Stars	7
Forks	0
Bundled files	1
License	MIT
Links	Skill source Repository

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by kkunkunya on GitHub. Read the source before you install it.

Installation

Install the System Design AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/kkunkunya/ai-frontend-design-kit/tree/main/skills/system-design
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.

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/kkunkunya/ai-frontend-design-kit.git /tmp/ai-frontend-design-kit
mkdir -p .claude/skills
cp -r /tmp/ai-frontend-design-kit/skills/system-design .claude/skills/system-design

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable System Design 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 System Design 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 System Design 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.

系统架构决策

技能定位

本技能处理"系统怎么切"的问题：模块边界、数据流向、存储选型、API 契约、依赖拓扑、扩展点。

核心方法论：Alternatives Considered 强制 + trade-off 诚实。没有 alternatives 段的 Design Doc 是推销文档，不是设计文档。

何时进入

多模块项目或跨模块改造
存量系统新增模块（需评估是否破坏现有边界）
高风险集成（第三方服务、跨团队 API）
重大技术选型（数据库、消息队列、部署架构）
req-suite:project-spec 路由过来

何时不进入

前端技术栈选型（Next.js/Vite/状态管理）→ frontend-design-research
视觉/动效/组件库 → frontend-design-research
纯 UI 项目无后端 → 不需要本 skill
单模块小项目、local-light 模式 → 不需要本 skill

前置输入

从 req-suite:project-spec 获取：

knowledge/product-specs/PRODUCT-BRIEF.md（目标结果 + 非目标）
knowledge/references/research-*.md（调研结果，如有）
选择题清单中和架构相关的 🟡/🔴 项

核心流程


1. 结构影响分析 → 2. Design Doc / ADR → 3. 更新 ARCHITECTURE.md

步骤 1：结构影响分析

回答这些问题：

这次变化涉及哪些模块？
有没有模块不该碰（boundary violation）？
数据流方向是什么？有没有循环依赖？
失败模式是什么？一个模块挂了会影响什么？
有没有共享状态需要加锁/同步？

步骤 2：写 Design Doc 或 ADR

根据决策规模选用：

大 Design Doc（重要方案决策）

核心段（按社区最佳实践）：

段	写什么	质量标准
Context	背景事实 + 相关链接，不写观点	3-5 行，读完知道为什么在讨论这件事
Goals / Non-goals	要达成什么、明确不追求什么	可验证的 bullet 列表
Proposed Design	方案概述 + 关键架构图	Agent 读完能开始实现
Alternatives Considered	考虑过的其他方案 + 每个的 trade-off	这是核心。没有这段的 doc 是推销文档
Risks & Mitigations	已知风险和应对	每条 risk 有对应 mitigation
Success Criteria	怎么判断方案成功	可测量、可验证

规模：1-3 页小方案，5-10 页大方案。超过 10 页说明该拆。

产出写入 knowledge/design-docs/DESIGN-DOC-[标题].md。

小 ADR（单个决策记录）

适用于具体技术选择、配置决策、依赖选型。

markdown
# ADR-NNN: [决策标题]

## Status
Proposed | Accepted | Superseded by ADR-NNN | Deprecated

## Context
[驱动这个决策的背景力量、约束、需求]

## Decision
[选了什么、怎么做]

## Consequences
[好的后果 + 坏的后果 + trade-off]

一个文件一个决策，不合并。不修改已 Accepted 的 ADR，用新 ADR 替代。

产出写入 knowledge/design-docs/ADR-NNN-[标题].md。

步骤 3：更新 ARCHITECTURE.md

将稳定的结构事实写入项目根的 ARCHITECTURE.md（不是 knowledge/ 下的）：模块拓扑、关键依赖、数据流向。只写事实，不写决策理由（理由住在 Design Doc / ADR 里）。

与 frontend-design-research 的边界

两个 skill 是正交子域，不是上下层：

本 skill 管	frontend-design-research 管
模块边界、数据流、存储、API 契约	主语言、参考案例、动效采访、视觉 token
依赖拓扑、扩展点	前端技术栈（框架/路由/状态/构建）
跨层集成协议	组件库路由、动效资产库

边界冲突裁决：

前端技术栈（Next.js/Vite 等）→ 归 frontend-design-research（和视觉/动效紧密耦合）
API 契约 → 归本 skill（跨层协议）
全栈项目先跑本 skill 定系统骨架，再跑 frontend-design-research 定前端表达

知识库路由

触发	知识库条目	路径
结构影响分析	架构分层思考框架	~/note/01-AI工程/项目方案方法/架构分层思考框架.md
多方案对比	方案对比指南	~/note/01-AI工程/项目方案方法/方案对比指南.md
上游方法论	req-project-spec 方法论	~/note/01-AI工程/Agent系统/方法/02-需求与项目方案/从需求到项目方案：req-project-spec 方法论.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

references/packaged-knowledge/manifest.json1 KB

Frequently asked questions

What does the System Design AI skill do?

Why use System Design on TypingMind?

Because you install it once and use it with any model. System Design 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 System Design in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kkunkunya/ai-frontend-design-kit/tree/main/skills/system-design. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use System Design?

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 System Design?

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

Is the System Design AI skill free?

Yes. It is published on GitHub by kkunkunya under the MIT 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

系统架构决策

技能定位

何时进入

何时不进入

前置输入

核心流程

步骤 1：结构影响分析

步骤 2：写 Design Doc 或 ADR

大 Design Doc（重要方案决策）

小 ADR（单个决策记录）

步骤 3：更新 ARCHITECTURE.md

与 frontend-design-research 的边界

知识库路由

Bundled files

Frequently asked questions

Related AI Skills

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