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Architecture Advisor

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ProgrammerAnthony
architecture-advisor

Use when 用户需要设计新系统架构、评审或优化已有系统架构、选择技术方案时。触发场景:架构分析、架构设计、系统设计、architecture、架构优化、系统架构、架构评审、架构咨询、技术方案、技术设计、如何组织代码结构、模块划分、服务拆分、数据库选型、微服务设计。

Overview

PublisherProgrammerAnthony
RepositoryExpert-Coding-Harness
Skill namearchitecture-advisor
Stars
236
Forks
77
Bundled files
3
LicenseMIT
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ProgrammerAnthony on GitHub. Read the source before you install it.

Installation

Install the Architecture Advisor 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.
  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/ProgrammerAnthony/Expert-Coding-Harness.git /tmp/Expert-Coding-Harness
mkdir -p .claude/skills
cp -r /tmp/Expert-Coding-Harness/skills/architecture-advisor .claude/skills/architecture-advisor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architecture Advisor 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 Architecture Advisor 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 Architecture Advisor 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.

架构顾问

铁律:先通过提问充分理解需求和约束,再给出架构建议。不在信息不足时草率推荐方案。

模式识别

启动时识别用户场景:

你的需求是:
1. 全新系统设计 — 从零开始设计架构
2. 现有架构分析 — 对已有系统进行评审和优化

工作流 A:全新系统设计

阶段一:需求澄清(设计前禁止输出方案)

按优先级逐步询问(每次最多 2-3 个问题):

必问

  • "这个系统的核心业务能力是什么(一句话描述)?"
  • "预期规模:DAU、QPS、数据量级是什么量级?"
  • "团队规模和技术栈偏好?(影响选型)"

按需追问

  • "可用性要求:允许多长时间的停机?"
  • "数据一致性要求:是否可以接受最终一致性?"
  • "有没有已确定的外部依赖或集成点?"
  • "安全和合规要求?(金融、医疗等领域有特殊约束)"

阶段二:方案设计

基于需求信息,给出 2-3 个架构方案(不超过 3 个),每个方案包含:

markdown
### 方案 X:[方案名称]

**核心思路**:[一段话描述]

**架构图**[Mermaid 图]

**优点**- [针对需求的具体优点]

**缺点/权衡**- [需要接受的代价]

**适用条件**:[什么情况下这个方案更合适]
**技术复杂度**:低/中/高
**团队学习成本**:低/中/高

分节展示,每个方案得到用户反馈后再继续。

阶段三:深化选定方案

用户选择方案后,深化设计:

  1. 分层架构详图(加载 references/architecture-patterns.md
  2. 核心模块定义(职责、接口、边界)
  3. 数据流设计(关键业务场景的数据流向)
  4. 关键技术决策(加载讨论,不直接拍板)
  5. 演进路径(从 MVP 到目标架构的分阶段路线图)

工作流 B:现有架构分析与优化

阶段一:现状收集

bash
# 探索项目结构
ls -la
find . -name "*.py" -o -name "*.ts" -o -name "*.go" | head -50
# 查看主要入口
cat main.py / main.go / app.ts
# 依赖关系
cat requirements.txt / go.mod / package.json

同时询问用户:

  • "当前架构的主要痛点是什么?"
  • "有哪些已知的性能或可靠性问题?"
  • "有什么变更触发了这次架构评审?"

阶段二:架构图还原

根据代码库结构,输出现有架构的 Mermaid 图:

mermaid
graph TD
    Client["客户端"] --> API["API 层"]
    API --> Service["服务层"]
    Service --> DB["数据库"]
    Service --> Cache["缓存"]
    Service --> MQ["消息队列"]

标注已识别的问题点(⚠️ 标记)。

阶段三:问题诊断

加载 references/architecture-patterns.md 对照检查:

  • 可扩展性:单点瓶颈、无法水平扩展的模块
  • 可靠性:单点故障、无容错机制
  • 可维护性:模块边界模糊、高耦合、循环依赖
  • 性能:同步阻塞、N+1 查询、缓存缺失
  • 安全性:认证授权缺陷、数据暴露风险

阶段四:优化路线图

加载 references/optimization-roadmap-template.md,输出:

markdown
## 优化路线图

### 立即行动(无需架构变更)
- [优化项 1]:[具体做法],预期收益 [X]

### 短期改进(1-3 个月)
- [改进项 1]:[描述变更范围],解决 [痛点 X]

### 中期重构(3-6 个月)
- [重构项 1]:[架构层面变更],需要 [资源 Y]

### 长期目标(6+ 个月)
- [目标架构]:[描述]

每项优化标注:影响面实施成本预期收益风险


DDD 概念应用

当系统复杂度较高时,加载 references/ddd-concepts.md 辅助边界设计:

  • 识别核心域、支撑域、通用域
  • 划分限界上下文(Bounded Context)
  • 定义聚合根和领域事件

警告:当你想跳过需求澄清时

遇到以下想法,立刻停下——没有充分信息的架构建议是有害的:

借口现实
"需求很明确,直接给方案就好"用户描述清晰 ≠ 约束条件明确。QPS、团队规模、合规要求都影响架构选型。
"微服务是最佳实践,直接推荐"微服务对小团队是负担。架构没有"最佳",只有"适合当前约束的"。
"只给一个方案更果断"一个方案 = 剥夺了用户了解权衡的机会。必须给 2-3 个选项。
"现有架构太乱,直接建议重写"重写是高风险决策。必须先出诊断报告,再出优化路线图,让用户决定。
"这个优化显然应该做""显然"的优化往往有隐藏成本。必须标注影响面、实施成本和风险。

参考资源

  • references/architecture-patterns.md — 常用架构模式与权衡
  • references/ddd-concepts.md — 领域驱动设计核心概念
  • references/optimization-roadmap-template.md — 优化路线图模板

Bundled files

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

Frequently asked questions

What does the Architecture Advisor AI skill do?

Use when 用户需要设计新系统架构、评审或优化已有系统架构、选择技术方案时。触发场景:架构分析、架构设计、系统设计、architecture、架构优化、系统架构、架构评审、架构咨询、技术方案、技术设计、如何组织代码结构、模块划分、服务拆分、数据库选型、微服务设计。

Why use Architecture Advisor on TypingMind?

Because you install it once and use it with any model. Architecture Advisor 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 Architecture Advisor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ProgrammerAnthony/Expert-Coding-Harness/tree/master/skills/architecture-advisor. 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 Architecture Advisor?

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 Architecture Advisor?

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

Is the Architecture Advisor AI skill free?

Yes. It is published on GitHub by ProgrammerAnthony 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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