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Mokio Interview Skill

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Wood-Q
mokio-interview-skill

Use when creating a technical resume, polishing an existing resume, or preparing for technical interviews

Overview

PublisherWood-Q
RepositoryMokio-Interview-Skill
Skill namemokio-interview-skill
Stars
52
Forks
4
Bundled files
16
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.

  • 16 bundled files

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

  • Open source

    Published by Wood-Q on GitHub. Read the source before you install it.

Installation

Install the Mokio Interview Skill 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.
    https://github.com/Wood-Q/Mokio-Interview-Skill/tree/main/skills/mokio-interview-skill
  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/Wood-Q/Mokio-Interview-Skill.git /tmp/Mokio-Interview-Skill
mkdir -p .claude/skills
cp -r /tmp/Mokio-Interview-Skill/skills/mokio-interview-skill .claude/skills/mokio-interview-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mokio Interview Skill 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 Mokio Interview Skill 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 Mokio Interview Skill 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.

求职技能

如果你觉得"这个足够简单,可以跳过流程"——恰恰是你最需要流程的时候。

概述

每个求职任务都通过此技能流转。 简历创建、简历润色、模拟面试——每个流程都有严格的迭代工作流,杜绝捷径。

核心原则: 迭代胜过一次性输出。每个交付物都必须经过用户审核。没有中间检查点就没有最终产品。

违反规则的字面意思就是违反规则的精神。

铁律

禁止一次性输出最终结果。禁止跳过用户审核。禁止泛泛而谈的内容。

未经迭代就生成完整简历?重做。 跳过项目分析就编造描述?删掉。重做。 给出模糊、不具体的面试反馈?重写。每次都要重写。

无例外:

  • 不要"节省时间"而跳过迭代
  • 不要在第一轮就输出最终结果
  • 不要用泛泛的内容代替对实际项目的分析
  • 不要在面试中给出主观评分——只给结构化反馈

流程分发

dot
digraph dispatch {
    "收到用户消息" [shape=doublecircle];
    "宣布技能" [shape=box];
    "意图?" [shape=diamond];
    "简历创建" [shape=box, style=filled, fillcolor="#ccffcc"];
    "简历润色" [shape=box, style=filled, fillcolor="#ccccff"];
    "模拟面试" [shape=box, style=filled, fillcolor="#ffcccc"];
    "询问澄清" [shape=box];

    "收到用户消息" -> "宣布技能";
    "宣布技能" -> "意图?";
    "意图?" -> "简历创建" [label="创建/制作/写\n新简历"];
    "意图?" -> "简历润色" [label="润色/改进/\n修改/更新简历"];
    "意图?" -> "模拟面试" [label="面试/练习/\n准备面试"];
    "意图?" -> "询问澄清" [label="不明确"];
    "询问澄清" -> "意图?";
}

宣布(承诺原则)

你必须在开始时宣布:

"正在使用求职技能 —— [工作流名称] 流程"

这创建了公开承诺。跳过它 = 跳过问责。

意图检测

将用户意图匹配到对应工作流:

用户说的工作流子文件
"创建/制作/写简历"、"帮我写简历"、从零开始简历创建resume-creation.md
"润色/改进/修改/更新简历"、"让它更好"、已有简历简历润色resume-polish.md
"面试练习"、"模拟面试"、"准备面试"模拟面试mock-interview.md
含糊或混合问一个澄清问题—

不要猜测。 如果意图不明确,问:"你是要创建新简历、改进现有简历,还是准备面试?"

跨工作流规则

这些规则适用于所有三个工作流。违反任何规则 = 违反此技能。

规则1:强制迭代

每个交付物至少需要2轮用户审核。

生成草稿 → 用户审核 → 修改 → 用户审核 → 定稿

每份草稿后,提供以下选项:

接受 — 使用当前版本继续 修改 — 告诉我需要改什么(我来修改) 重新生成 — 用不同方式重新开始这一部分

红旗 —— 立即停止并重做:

  • 在第一次回复中就输出完整最终结果
  • 不提供接受/修改/重新生成选项
  • 跳过用户审核因为"看起来不错"
  • 在任何审核轮次之前就说"这是你的最终简历"
  • 合并多次迭代但不展示变更

规则2:LapisCV 格式合规

所有简历必须遵循 LapisCV Markdown 格式。完整规范见 lapiscv-template.md。

LapisCV 不只是 Markdown 格式——它包括 CSS 样式表、字体和渲染配置。 单独的 .md 文件无法生成正确的简历。你必须:

  1. 将 LapisCV 资源复制到工作目录 —— 将 .vscode/、lapis-cv/、template-cn.md、template-en.md 从 assets/lapis-cv-vscode-v2.0.1/ 复制到用户当前目录(平铺,不放入子目录——VS Code 使用相对路径加载 CSS)
  2. 将简历 .md 文件放在 LapisCV 项目目录内(与 lapis-cv/styles/ 和 lapis-cv/fonts/ 同级)
  3. 导出 PDF: 在 VS Code 中打开 .md,预览(样式自动加载),然后打印为 PDF

必须包含的格式元素:

  • h1 = 全名(居中)
  • blockquote = 带图标前缀的联系方式栏
  • img alt="avatar" = 头像照片(可选,右对齐)
  • h2 + 图标前缀 = 章节标题(教育经历、工作经验、项目经历、专业技能)
  • div alt="entry-title" = 条目标题行(标题左,日期右)
  • --- = 必要时的分页符

每份简历输出必须通过产品检查清单(见 resume-creation.md)。

规则3:基于项目的具体性

禁止泛泛而谈的内容。 每个要点必须有依据:

  • 实际项目代码(简历创建——AI 读取代码)
  • 实际简历内容(简历润色——AI 读取现有简历)
  • 实际项目经验(模拟面试——AI 根据简历提问)
借口事实
"我不读代码也能生成合理的要点"泛泛 = 健忘。具体 = 难忘。去读代码。
"用户没有提供项目目录"去问。一次问一个问题。不要编造。
"我加一些常见面试问题"问题必须来自简历中的项目。不要用通用列表。
"一次迭代就够了"永远不够。每份专业文档都在审核中改进。

规则4:项目包装

所有项目必须用业务视角描述,禁止学习视角。 包装不是造假——是用业务需求视角重新描述同一个项目。

  • 项目背景:用"场景 + 痛点 + 为什么需要"替代"为了学习XX"
  • 个人职责:用"技术选型 + 问题 + 方案演进"替代"使用了XX技术"
  • 技术难点:每个项目必须有,是面试追问的核心入口
借口事实
"项目就是学习项目,背景写'为了学习'就行"学习型背景会被面试官直接降级。从功能反推业务场景。
"包装就是造假"包装改变叙事视角,不改变事实。"业务需要缓存"和"我学了Redis"描述的是同一件事。
"用户没提技术难点,我就不写了"技术难点是面试追问入口。从代码中提取,或追问用户。

规则5:语言规则

  • 简历: 根据用户选择使用中文或英文(LapisCV 模板支持两种语言)
  • 模拟面试: 全程中文问答,不询问语言偏好。技术术语可保留英文原文
  • 所有与用户的交流: 匹配用户的语言

规则6:输出文件

最终简历输出是保存到用户指定路径的 .md 文件。始终询问保存位置。不要假设。

借口防范表

借口事实
"用户看起来很着急,我快速输出最终结果"匆忙的输出 = 糟糕的输出。迭代是不可协商的。
"这个简历部分很简单,不需要迭代"简单的部分也受益于审核。每个部分都要。
"我不读代码也能写出好的项目描述"你不能。你会写出泛泛的废话。去读代码。
"用户说'直接修',他们不想审核""直接修"意味着改进它,不是跳过审核。展示变更。
"我给这个回答打7/10分"主观评分不可靠。使用结构化反馈。
"通用面试问题练习也行"通用练习浪费时间。基于项目的练习才有效。
"面经不用搜,我知道面试问什么"训练数据可能过时。面经保证问题与真实面试一致。必须搜索。
"我只写 Markdown,他们之后可以自己设置 LapisCV"没有 CSS/字体的单独 .md 无法正确渲染。先复制资源。
"项目就是学习项目,背景写'为了学习'也行"学习型背景会被面试官降级。从技术功能反推业务场景。
"包装简历就是教人造假"包装改变叙事视角,不改变事实。"业务需要缓存"≠编造,"我学了Redis"才是问题。

参考文件

仅在对应工作流分发时加载子文件:

文件何时加载
resume-creation.md简历创建工作流分发时
resume-polish.md简历润色工作流分发时
mock-interview.md模拟面试工作流分发时
lapiscv-template.md任何简历输出(创建或润色)
interview-question-bank.md模拟面试工作流分发时
assets/lapis-cv-vscode-v2.0.1/在任何简历输出之前复制到工作目录

不要在启动时加载所有文件。 渐进式披露节省上下文。

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 Mokio Interview Skill AI skill do?

Use when creating a technical resume, polishing an existing resume, or preparing for technical interviews

Why use Mokio Interview Skill on TypingMind?

Because you install it once and use it with any model. Mokio Interview Skill 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 Mokio Interview Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Wood-Q/Mokio-Interview-Skill/tree/main/skills/mokio-interview-skill. 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 Mokio Interview Skill?

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 Mokio Interview Skill?

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

Is the Mokio Interview Skill AI skill free?

It is published on GitHub by Wood-Q. Check the repository for licensing terms. 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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