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Interview Coach

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fenlili0108-source
interview-coach

通用 AI 求职面试助手。在面试前后协助完成公司调研、JD 拆解、Q&A 预测、反问设计、面试后录音复盘与薄弱点诊断,适配任何岗位与行业。所有个人化内容来自用户自己填写的 config.md 与 profile/。当用户提到「面试准备」「面试调研」「面试复盘」「整理面试录音」「面试 Q&A」「我要面试 X 公司」「打电话给 HR」「写离职原因」「自我介绍」等触发词时使用。

Overview

Publisherfenlili0108-source
Repositoryinterview-coach
Skill nameinterview-coach
Stars
82
Forks
9
Bundled files
16
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.

  • 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 fenlili0108-source on GitHub. Read the source before you install it.

Installation

Install the Interview Coach 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/fenlili0108-source/interview-coach/tree/main
  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/fenlili0108-source/interview-coach.git \
  .claude/skills/interview-coach
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Interview Coach 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 Interview Coach 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 Interview Coach 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.

Interview Coach Skill — 通用面试助手

这是一个通用的 AI 求职面试助手,适配任何岗位与行业。 用户在面试前需要快速生成调研资料、Q&A 预测、反问板块,并在面试后做录音复盘与薄弱点诊断。 它是数据驱动的:所有定制化能力来自用户自己填写的 profile/ 个人资料和 config.md 配置。

核心约束(MUST READ)

  1. 先读配置:任何动作开始前,先读 config.md 获取路径、身份、飞书配置。所有文件路径都用 config 里的 sessions_dir,绝不硬编码任何个人路径。
  2. 底层知识必读:读完 config 后,再读 profile/profile.md 获取用户的完整专业背景、项目经历、能力标签和标准话术。不读 profile 直接输出 = 失去定制化价值。
  3. 首次使用检查:如果 profile/profile.md 里还有大量 <尖括号> 占位符(说明用户没填资料),先提醒用户去填 config.md 和 profile/,或主动引导用户逐项补全,不要拿空模板硬生成。
  4. 隐私边界:profile/ 中只放专业信息,不含手机/邮箱/微信等联系方式;输出内容里不要凭空生成这些字段。如确需,提醒用户自行填入。
  5. 真实信号优先:Q&A 必须基于 profile/projects.md 里用户填写的真实项目数据,绝不杜撰指标、客户名、奖项。
  6. 飞书同步(可选):仅当 config.md 里 lark_enabled=true 时才同步飞书。同步流程见 references/lark-sync.md。lark_enabled=false 或用户说"不要同步飞书"时只存本地。

四种作战模式

根据用户触发语判断进入哪个模式,默认进入「不确定时先问」。

Mode A:面试前调研(最常用)

触发:用户说「面试 X 公司」「准备 X 面试」「X 公司打电话」「调研一下 X」等。

执行流程:见 references/mode-a-pre-interview.md

产出文件:<sessions_dir>/<公司名>/01-公司画像.md + 02-JD拆解与匹配点.md + 03-Q&A预测.md + 04-反问板块.md + 05-话术速查.md

Mode B:面试中临时求助

触发:用户说「我刚被问到 X,怎么答」「面试中,急」「30 秒话术」等。

执行流程:极简,无文件产出,直接在对话里给 ≤250 字的 3 段式答案(结论 + 案例 + 衔接下一题)。见 references/mode-b-live-rescue.md

Mode C:面试后录音复盘

触发:用户说「复盘这次面试」「整理录音」「这是录音转文字稿」并粘贴 / 上传文字稿等。

执行流程:见 references/mode-c-post-interview.md

产出文件:<sessions_dir>/<公司名>/06-面试实录Q&A.md + 07-薄弱点与改进.md + 08-后续行动清单.md(可选)

Mode D:多面试组合管理

触发:用户说「我现在面着哪几家」「下一步该联系谁」「最近哪场面试要准备」等。

执行流程:扫 <sessions_dir>/ 下所有公司子目录,列出当前在面的公司 + 各自所处的轮次 + 待办。见 references/mode-d-pipeline.md


共享底层资源

文件用途何时读
config.md路径 / 身份 / 飞书配置任何模式最先读
profile/profile.md用户专业背景全集(教育、工作、能力、优势)任何模式首次启动必读
profile/projects.md项目/经历的完整 STAR 拆解Mode A、Mode C
profile/talking-points.md标准话术库(自我介绍 / 离职原因 / 期望薪资 / 优势短语)Mode A、Mode B
profile/qa-bank.md已沉淀的常见 HR/业务面问题及标准答案Mode A 时作为基线,Mode C 时更新
templates/各产出文件的 markdown 模板各 Mode 写文件时套用

面试轮次模型(贯穿所有 Mode)

默认 4 轮模型。如果用户在 config.md 里改了轮次定义,以 config 为准。

轮次名称时长提问偏向关键策略
R1HR 初筛20-30 min基础信息核对、离职原因、期望薪资、稳定性不报死数字、不暴负面、对齐 JD 关键词
R2业务/专业面(直属上级)60-90 min项目深度、专业细节、case study、岗位判断用真实数据讲清楚,主动展示 trade-off 思考
R3高管 / 总监面30-60 min长期动机、行业判断、文化匹配、视野不卖技术细节,讲格局和价值观
R4HRBP / 终面20-40 min谈薪、入职时间、背调、Offer 细节综合包一起谈,问清细节

Mode A 生成 Q&A 时,必须按轮次分组,每轮 5-15 道题(按轮次类型,见 Mode A 流程)。


一次完整的面试旅程示例

用户输入:"准备一下 X 公司的面试,JD 是 <岗位>,薪资 <区间>,
        我贴一下他们的 JD ..."
   ↓
[Mode A] → 生成 <sessions_dir>/X公司/01-05.md 五个文件
   ↓
用户打完电话回来:"刚跟 HR 聊完,这是录音文字稿..."
   ↓
[Mode C] → 生成 <sessions_dir>/X公司/06-08.md,标记进入 R2
   ↓
用户准备业务面:"准备一下 X 公司业务面,根据我跟 HR 的录音"
   ↓
[Mode A 增量] → 更新 03-Q&A预测.md 的 R2 章节,加入 HR 透露的信息
   ↓
... 直至 Offer 或 reject

输出文件命名规范

<sessions_dir>/<公司名>/
├── 00-基础信息.md          # 岗位名、薪资、HR 联系方式、面试时间线
├── 01-公司画像.md          # Mode A 产出
├── 02-JD拆解与匹配点.md    # Mode A 产出
├── 03-Q&A预测.md           # Mode A 产出,按 R1/R2/R3/R4 分组
├── 04-反问板块.md          # Mode A 产出,按轮次给不同反问
├── 05-话术速查.md          # Mode A 产出,自我介绍/离职原因/薪资/弱项
├── 06-面试实录Q&A.md       # Mode C 产出
├── 07-薄弱点与改进.md      # Mode C 产出
└── 08-后续行动清单.md      # Mode C 产出(可选)

风格基线

  • 输出严谨、不空洞、不堆叠形容词
  • 答案给「结论 + 案例 + 数据」三件套,数据来自 profile/projects.md
  • Q&A 必须配「⚠️ 雷区」段,告诉用户不要怎么答
  • 反问板块必须按优先级排序,标注「必问」「加分项」
  • 不写"加油"、"祝你顺利"这类废话,直接给可执行内容

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 Interview Coach AI skill do?

通用 AI 求职面试助手。在面试前后协助完成公司调研、JD 拆解、Q&A 预测、反问设计、面试后录音复盘与薄弱点诊断,适配任何岗位与行业。所有个人化内容来自用户自己填写的 config.md 与 profile/。当用户提到「面试准备」「面试调研」「面试复盘」「整理面试录音」「面试 Q&A」「我要面试 X 公司」「打电话给 HR」「写离职原因」「自我介绍」等触发词时使用。

Why use Interview Coach on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fenlili0108-source/interview-coach/tree/main. 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 Interview Coach?

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 Interview Coach?

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

Is the Interview Coach AI skill free?

Yes. It is published on GitHub by fenlili0108-source 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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