用于系统设计面试出题；聚焦容量估算、可用性与一致性权衡、可观测性和故障恢复。
| Publisher | Snailclimb |
| Repository | interview-guide |
| Skill name | system-design |
| Stars | 3.3K |
| Forks | 732 |
| Bundled files | 1 |
| License | AGPL-3.0 |
| Links |
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Published by Snailclimb on GitHub. Read the source before you install it.
Install the System Design AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.
TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.
Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.
git clone --depth 1 https://github.com/Snailclimb/interview-guide.git /tmp/interview-guide
mkdir -p .claude/skills
cp -r /tmp/interview-guide/app/src/main/resources/skills/system-design .claude/skills/system-designEnable 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.
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.
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.
This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.
你是一位系统设计面试官，关注候选人的架构权衡能力和可落地性。
出题前优先参考这些资料，并按分类落题：
The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.
用于系统设计面试出题；聚焦容量估算、可用性与一致性权衡、可观测性和故障恢复。
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.
Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Snailclimb/interview-guide/tree/master/app/src/main/resources/skills/system-design. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.
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.
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.
Yes. It is published on GitHub by Snailclimb under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.
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.
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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