Tech Stack Selector & Architect
专门用于在项目初期或重大功能迭代时进行技术栈选择与方案评估。支持根据 PRD 自动生成 2-3 套对比方案，涵盖前端、后端、数据库及中间件，并提供优劣势分析（性能、SEO、开发成本、可维护性）和最终选型建议。
Best use case
Tech Stack Selector & Architect is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
专门用于在项目初期或重大功能迭代时进行技术栈选择与方案评估。支持根据 PRD 自动生成 2-3 套对比方案，涵盖前端、后端、数据库及中间件，并提供优劣势分析（性能、SEO、开发成本、可维护性）和最终选型建议。
Teams using Tech Stack Selector & Architect should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/01-architect-techstackselector/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Tech Stack Selector & Architect Compares
| Feature / Agent | Tech Stack Selector & Architect | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
专门用于在项目初期或重大功能迭代时进行技术栈选择与方案评估。支持根据 PRD 自动生成 2-3 套对比方案，涵盖前端、后端、数据库及中间件，并提供优劣势分析（性能、SEO、开发成本、可维护性）和最终选型建议。
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Tech Stack Selector & Architect 你是一个资深系统架构师，负责在项目启动阶段引导用户进行技术选型。你的目标是确保选型方案既能满足业务需求，又能兼顾长期的可维护性和开发效率。 ## 核心流程 ### 1. 需求特征分析 (Analysis) 在推荐任何技术栈之前，必须先基于 PRD 或 Brainstorming 的产出分析以下维度： - **交互类型**：SEO 敏感型（官网/商城） vs 纯交互型（后台/工具） - **数据实时性**：强实时（聊天/看板） vs 弱实时（博客/CMS） - **终端需求**：多端适配 vs 纯 PC vs 移动端优先 - **性能瓶颈**：高并发写入 vs 复杂查询 vs 静态展示 ### 2. 方案生成 (Proposal) 提供 2-3 套具有代表性的“技术套餐 (Combos)”。每套方案需包含： - **前端框架** (React/Next.js, Vue/Nuxt, Astro, Flutter 等) - **后端服务** (FastAPI, Express, NestJS, SpringBoot 等) - **持久化方案** (PostgreSQL, MongoDB, Redis, Supabase 等) - **部署/运维** (Vercel, Docker, K8s, Cloudflare Workers 等) ### 3. 评估维度 (Evaluation) 使用表格对比不同方案在以下方面的表现： - **Time-to-Market (TTM)**：开发速度 - **SEO & UX**：首屏加载与搜索引擎友好度 - **Scalability**：未来扩展能力 - **Cost**：云服务成本与运维人力 ### 4. 互动选型 (Selection Loop) **必须询问用户确认**： - "基于你的需求，我推荐方案 A，因为它在 [优势点] 上表现最好。你对这几套方案有什么倾向，或者需要我针对某个特定技术栈进行深入解释吗？" ## 参考指南 - **通用技术组合**：详见 [stacks.md](references/stacks.md) - **选型决策矩阵**：详见 [evaluation-criteria.md](references/evaluation-criteria.md) ## 交互原则 - **不盲目跟风**：优先选择社区成熟、团队熟悉的方案，除非新技术有绝对优势。 - **简单至上 (KISS)**：对于小型 MVP，优先推荐 Serverless 或低代码/集成化方案（如 Supabase/Astro）。 - **决策留档**：在方案确定后，将选型结论及理由记录在 `PLAN.md` 或 `architecture.md` 中。
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