如何將 Claude 用作軟體架構師:Viktor Skill 指南

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Skill · .md · 可與 Claude & ChatGPT 搭配使用
Viktor - 軟體架構師 AI Skill
Viktor - 軟體架構師 AI Skill
$29此 Skill 相較於 $200架構顧問,每小時

將一個檔案放入你的 AI 中,它就能像軟體架構師一樣運作 - 規劃設計、說明取捨,並記錄決策。不需訂閱。永久屬於你。

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撰寫程式碼是簡單的部分。在任何人開始撰寫程式碼之前,決定系統應該如何成形,才是專案悄悄成功或失敗的關鍵。當人們用 Claude 作為軟體架構師 搭配簡短的 prompt 時,他們得到的是一個圖表般的答案,卻沒有解釋為什麼要那樣建構。真正的 AI 軟體架構師 會先處理取捨,因為架構是一門選擇你寧願面對哪些問題的學問。

Viktor 就是那位架構師。他是一種設計角色,只要載入 Claude、ChatGPT 或任何 AI 聊天工具一次即可使用,而且他拒絕直接交出單一的「最佳」設計。他想知道你要建構的規模、擁有的團隊,以及你面臨的限制 - 因為兩人新創適用的正確架構,對銀行來說可能就是錯的,反之亦然。

為什麼通用的 AI 架構建議不夠完善

沒有引導時,聊天機器人會用時髦的技術包裝一個自信滿滿的答案,卻不說明讓它成為真正選擇的取捨。它忽略規模與限制,對週末專案和企業專案一視同仁地推薦相同的微服務與 Kubernetes 技術堆疊。沒有明確缺點的設計不是架構,而是一份願望清單。

Viktor Skill 有哪些變化

Viktor 會先詢問規模、團隊大小、預算與限制條件,再提出任何建議,接著提供列明各項取捨的選項 - 包括每個選項的成本、複雜度與失效模式。他會明確分析可擴展性、成本與可維護性,並記錄決策及其理由,讓這個選擇即使在做決策的人員更替後仍然站得住腳。你得到的是一套經得起檢視的設計,而不是追逐潮流的設計。

它實際會產出什麼

包含取捨的架構選項、系統與資料流程說明、附帶選擇理由的技術選型、可供保存的架構決策紀錄,以及擴展性與風險分析。描述你正在建置的內容及其限制條件,他會為你整理出各種路徑 - 並告訴你每條路徑日後要付出的代價。

如何充分發揮它的效用

告訴他真實的限制條件 - 預期規模、團隊大小、預算、截止期限 - 因為這些才是優秀架構與泛泛而談之間的差異。請他提供多個選項,而不是單一答案,讓你能在看清取捨的情況下做出選擇。也請他記錄決策及其原因,這樣未來的你能記住當時的思考,而不必再次爭論同一件事。

適用對象

負責做設計決策的技術主管與資深工程師、在承諾投入前決定如何建置產品的創辦人,以及希望擁有一位思考夥伴的架構師。它可以搭配 Claude、ChatGPT,或任何接受系統 prompt 的 AI 聊天工具使用。至於實際建置,技術與開發 Skill 集合涵蓋程式設計、程式碼審查與交付 - 每個 Skill 都是專注的助理,而非通用聊天機器人。

相關 Skill 指南

常見問題

Can Claude design a software architecture?+

It can produce a design, and the design will be greenfield and maximal, because the material it learned from was written by people describing systems that worked at a scale worth writing about. Nobody publishes a post about the boring monolith that is still fine. So you get the architecture of a company larger than yours, on an empty field, with the foreclosures left out. That is correctable, but not by asking better questions about the design - only by changing what the model is allowed to assume.

Why does AI always suggest microservices and Kubernetes?+

Because those are the write-ups of organisations that genuinely needed them, and there are no write-ups of the companies that did fine without. The average of the literature is therefore an architecture for a larger company. The cheapest correction is to ask for the same system at one tenth of the load you stated, with the same team, then ask what was removed and at what specific measurable point each removed piece would need to come back.

What is an architecture, really?+

A list of things you have decided you will not be able to do. Choosing eventual consistency forecloses certain guarantees, choosing a monolith forecloses independent deployment, choosing a particular store forecloses certain shapes of growth. The foreclosures are the decision. A model leads with capabilities every time and will not volunteer what you gave up, so ask for the foreclosure list before the enablement list - and treat an option that appears to have no downsides as suspicious rather than as a recommendation.

Why are AI architecture diagrams misleading?+

Because the boxes are the easy part. A service that does one clear thing is a solved problem. All of the difficulty is in the arrows, and an arrow is a claim that two things can talk which the diagram never justifies. Synchronous or asynchronous, what happens on failure, whether a retry is safe, what the timeout is, whether order matters and what guarantees it, what still works if this arrow is down for an hour. Interrogate the arrows and the design usually changes, often collapsing back into fewer boxes.

What is AI genuinely good at in architecture work?+

Generating the failure space. Asking what are all the ways this can go wrong is a recall problem across an enormous body of postmortems and incident write-ups, which is exactly what these tools are for, and it will surface the failure mode you would otherwise have found in production in eight months. What it cannot do is rank them, because ranking needs your traffic, your team, your tolerance and your money. It generates the list, you order it.

Can it estimate how much load my design will handle?+

It will give you a number and it has no idea. Throughput, latency and capacity depend on your hardware, your data shape, your query patterns and your access distribution, and the only way to get them is to measure. Treat any performance figure it offers as a placeholder where a load test should go. The same applies to service quotas, managed-database limits, instance sizes and cloud pricing, all of which change and all of which it answers from whatever it last saw.

Why do AI designs never include a migration path?+

Because greenfield designs do not need one, and greenfield is what the corpus describes. In reality a target architecture with no route to it is a wish, and the migration is where most of the cost lives. Ask for the path in steps that each ship independently with the system live, and apply one test to every step: could we stop here permanently and still be better off than we are now? If the answer is no, the step is too big. Also ask where the point of no return is.

How do I install the Viktor skill?+

The download is a ZIP with SKILL.md at the root of the archive rather than inside a nested folder, which is the usual reason an upload fails. In the Claude desktop app open Customize, then Skills, upload the ZIP and toggle it on. Skills need code execution enabled, under Settings then Capabilities. Anthropic's help centre currently lists Skills on Free, Pro, Max, Team and Enterprise, while its Academy tutorial lists Pro, Max, Team and Enterprise, so if you are on the free plan check Settings then Capabilities for your own account rather than trusting either page. In ChatGPT or Gemini there is no upload step, so open SKILL.md, copy the contents and paste them into custom instructions.

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