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Interview Knowledge Track

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ProgrammerAnthony
interview-knowledge-track

三阶段面试知识点追踪:英文命令 split-knowledge(知识点拆分与 KB-INDEX)、research-topic(按主题检索落盘 opensource + interview-drill)、synthesize-topic(结合 KB 原文与第二步产出,写入经历绑定的架构与面试 Markdown)。别名 kp-split、topic-research、topic-synthesize。中文触发:知识点拆分、按主题检索、业务梳理、薄弱点追踪、KB-INDEX。默认工作目录 interview-knowledge-track/;不自动改写用户工作区文件,除非用户明确要求。自包含,不绑定特定简历路径或仓库结构。

Overview

PublisherProgrammerAnthony
RepositoryExpert-Coding-Harness
Skill nameinterview-knowledge-track
Stars
236
Forks
77
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by ProgrammerAnthony on GitHub. Read the source before you install it.

Installation

Install the Interview Knowledge Track 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.
  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/ProgrammerAnthony/Expert-Coding-Harness.git /tmp/Expert-Coding-Harness
mkdir -p .claude/skills
cp -r /tmp/Expert-Coding-Harness/skills/interview-knowledge-track .claude/skills/interview-knowledge-track
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Interview Knowledge Track 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 Knowledge Track 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 Knowledge Track 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 Knowledge Track(面试知识点三阶段追踪)

权威切片与参考

  • 分步执行的详细指令以本 skill 目录下 prompts/ 为唯一必需依据。
  • 若工作区里另有用户自维护的「架构/面试」长提示词,仅可在不冲突时作语气参考;非必需,也不假定其路径或文件名。

目标与边界

  • 目标:用三条英文命令驱动「拆知识点 → 按点检索沉淀 → 结合候选人自述文本做架构+面试合成」,定位表述中的薄弱技术点。
  • 输入:第一步仅使用用户粘贴内容,或用户显式给出路径并要求读取的文件;假定、不自动加载工作区内任何其它文件。
  • 写操作范围:默认只在用户约定的工作根目录(见下)内创建/更新 KB-INDEX.md 与主题子目录下的 Markdown;把本 skill 与任意「默认简历文件」绑定。

英文命令(用户口述即可)

命令含义
split-knowledge第一步:拆分知识点,生成/更新索引 KB-INDEX.md
research-topic第二步:对某一知识点检索,写入该主题目录下两个 MD,并回写索引状态
synthesize-topic第三步:在指定知识点目录下,依据索引原文 + 第二步产出,写「与本人经历绑定」的两份 MD

别名(可选)kp-splitsplit-knowledgetopic-researchresearch-topictopic-synthesizesynthesize-topic

工作根目录与文件命名

  • 工作根目录:用户指定则用之;否则默认为项目根目录下 interview-knowledge-track/
  • 第一步产出<root>/KB-INDEX.md(用户可指定其他路径/文件名,但同一会话后续步骤必须能唯一定位该索引)。
  • 主题目录<root>/NN-slug/,例如 01-langchainNN 为两位序号,与知识单元表顺序一致;slug 小写、连字符。
  • 第二步文件(固定文件名):
    • opensource-and-architecture.md — 开源/博客/论文检索 + 架构与模块拆解,全文落盘
    • interview-drill.md — 精准关键词检索 + 概念补充 + 追问与参考答案,全文落盘
  • 第三步文件(与第二步同主题目录,除非用户另指定):
    • architecture-bound-to-resume.md
    • interview-bound-to-resume.md

第一步索引契约(KB-INDEX.md

生成或更新 KB-INDEX.md 时必须包含:

  1. 原文保留区:完整粘贴用户输入(或注明读取的文件路径 + 可选校验信息如段落范围/哈希),满足不丢失原信息。
  2. 知识单元表:每行至少包含:序号知识点名称slug来源原文摘录(精确到句)、状态对应目录
  3. 拆分规则:技术栈/框架/领域词拆为可独立检索单元;同一句话可对应多个单元(摘录可重复或附注「共现句」)。
  4. 元数据:生成日期、工作根路径。

状态枚举与回写规则

状态含义
pending尚未执行第二步
researched第二步已完成:NN-slug/ 下已写入 opensource-and-architecture.mdinterview-drill.md,且索引中 对应目录 已填写
synthesized第三步已完成:同目录下已写入 architecture-bound-to-resume.mdinterview-bound-to-resume.md
  • 第二步完成后:将该知识点行的 状态 改为 researched,并写回 对应目录(如 01-langchain)。
  • 第三步完成后:将该知识点行的 状态 改为 synthesized

Agent 执行要点

split-knowledge

  • 创建或更新 <root>/KB-INDEX.md,遵守上文契约。
  • 聊天中仅给出路径、主题数量与 slug 列表等短摘要。

research-topic

  • 用户需指定当前主题(序号、slug 或知识点名称)与工作根/索引路径(若与默认不同)。
  • 读取 prompts/step2-architecture.mdprompts/step2-interview-drill.md,使用 Web 检索 满足检索要求。
  • 将完整内容写入主题目录下两个 MD;聊天仅回复路径与 1–3 句摘要。
  • 更新 KB-INDEX.md 中该行状态为 researched

synthesize-topic

  • 强制输入顺序:① KB-INDEX.md(全文至少使用「原文保留区」+ 当前主题在知识单元表中的行);② 当前主题目录下第二步两个文件;③ prompts/step3-synthesis.md
  • 写入 architecture-bound-to-resume.mdinterview-bound-to-resume.md;聊天仅路径与极短摘要。
  • KB-INDEX.md 中该行状态改为 synthesized

质量自检(执行后自查)

  • 第一步:每条知识点是否在「原文保留区」中有逐字或可核对的摘录依据。
  • 第二步:两个 MD 是否均含检索关键词与来源/诚信声明;禁止伪造 GitHub 链接或论文。
  • 第三步:架构段落与每道面试题是否能回答「对应第一步哪一句/哪一项目」;技术与链路与第二步一致,无无出处杜撰。

流程示意

mermaid
flowchart LR
  input[用户粘贴文本或指定文件]
  split[split-knowledge]
  index[KB-INDEX.md]
  r2[research-topic]
  dir[NN-slug目录]
  os[opensource-and-architecture.md]
  iv[interview-drill.md]
  r3[synthesize-topic]
  ar[architecture-bound-to-resume.md]
  ir[interview-bound-to-resume.md]
  input --> split --> index
  index --> r2 --> dir
  r2 --> os
  r2 --> iv
  index --> r3
  os --> r3
  iv --> r3
  r3 --> ar
  r3 --> ir

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 Knowledge Track AI skill do?

三阶段面试知识点追踪:英文命令 split-knowledge(知识点拆分与 KB-INDEX)、research-topic(按主题检索落盘 opensource + interview-drill)、synthesize-topic(结合 KB 原文与第二步产出,写入经历绑定的架构与面试 Markdown)。别名 kp-split、topic-research、topic-synthesize。中文触发:知识点拆分、按主题检索、业务梳理、薄弱点追踪、KB-INDEX。默认工作目录 interview-knowledge-track/;不自动改写用户工作区文件,除非用户明确要求。自包含,不绑定特定简历路径或仓库结构。

Why use Interview Knowledge Track on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ProgrammerAnthony/Expert-Coding-Harness/tree/master/skills/interview-knowledge-track. 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 Knowledge Track?

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 Knowledge Track?

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

Is the Interview Knowledge Track AI skill free?

Yes. It is published on GitHub by ProgrammerAnthony 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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