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Repo Docs Zh

Community
YurunChen
repo-docs-zh

Generate and maintain repo-docs with Chinese as the primary reader language while preserving source identifiers for lookup. Use when the user asks for Chinese repo docs, mentions repo-docs-zh, wants repo documentation in Chinese, or wants an existing repo-docs package localized for Chinese readers.

Overview

PublisherYurunChen
Repositoryrepo-docs-skills
Skill namerepo-docs-zh
Stars
433
Forks
8
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Repo Docs Zh 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/YurunChen/repo-docs-skills/tree/main/docs/repo-docs-zh
  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/YurunChen/repo-docs-skills.git /tmp/repo-docs-skills
mkdir -p .claude/skills
cp -r /tmp/repo-docs-skills/docs/repo-docs-zh .claude/skills/repo-docs-zh
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Repo Docs Zh 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 Repo Docs Zh 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 Repo Docs Zh 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.

Repo-Docs ZH

Position

This is the Chinese-language overlay for repo-docs. Structure, page ownership, evidence rules, sync behavior, and validation come from ../repo-docs/SKILL.md. This overlay only changes language and Chinese reader experience.

Before acting, read:

  1. ../repo-docs/SKILL.md
  2. ../repo-docs/REFERENCE.md as the topic router only when detailed rules are needed
  3. The routed topic file only when needed
  4. ../repo-docs/EXAMPLES.md only for output shape or tone examples needed by the task

Core Principle

Chinese repo-docs are not English docs translated line by line. They should rebuild the reader's conceptual handles in Chinese.

Chinese carries understanding: what this thing is, why it exists, what happens, what details matter, and how to check it. English identifiers locate the source: paths, commands, fields, API names, class/function names, metric names, package names, and dataset names.

references/source-evidence.md is still the fixed Build evidence base in Chinese packages. Write claims, caveats, and reader-facing notes in Chinese; keep paths, commands, fields, source identifiers, and exact evidence locators in their source form.

标准包中的 code-map.md 写成“代码地图”。它在主 walkthrough 之后回答:范围内每个源码目录负责什么,里面哪些文件或符号最重要,某类修改应该从哪里开始,相关验证在哪里。中文负责说清职责和改动入口,英文路径与符号负责精确定位。

If writing exposes a weak claim or insufficient evidence, pause drafting and return to the project evidence. Re-inspect the relevant source path, tests, config, schema, data, command output, or artifact; if the evidence is still missing, label the claim as 推断 / 未确认, defer it explicitly, or leave it out.

Language Rules

  • Use Chinese for titles, explanations, reading paths, examples, caveats, and change-log entries.
  • Keep source identifiers exact in their source form: paths, commands, keys, API names, class/function names, metric names, error strings, package names, and dataset names.
  • First introduce repeated project terms as 中文名(English term) or 中文名(source identifier); later prefer the Chinese name.
  • README route section heading is ## 阅读路径.
  • Glossary columns are exactly 术语 | 项目里的意思 | 延伸阅读.
  • Glossary rows should focus on four term types: project-special common words, confusable concept families, external terms as used in this repo, and lightweight repeated names that do not deserve a module.
  • Narrative page evidence note: 证据状态:除特别标注外,本页基于当前源码已确认。

Chinese Handles And Source Locators

Use a Chinese reader handle before a source locator.

TypeUse in Chinese docs
读者句柄The narrative subject, such as “导入流程”, “会话层”, “结果汇总”, “运行脚本”.
源码定位符Paths, functions, classes, fields, commands, artifact paths. Link directly with a Chinese label when one locator supports one claim.
机制细节名Metrics, schema keys, tool parameters, artifact file names. Put them in the owning module when they help the reader understand the concept.
外部术语Terms like benchmark, agent, workspace, protocol, memory. First mention gets a Chinese handle; later prefer Chinese when the term appears in the inspected repo.

Default locator rule:

  • Prefer a Chinese visible label that links to the source file over showing the raw path as visible text.
  • Use inline code only for short identifiers that matter to the mechanism.
  • Use references/source-evidence.md for grouped claim evidence instead of creating extra evidence/source-map pages. code-map.md is the reader-facing code navigation page, not an evidence ledger.
  • Never make a long path the visible link text in narrative prose.

Good:

  • 运行脚本负责选择运行模式和输入来源。
  • 会话层先取上下文,再记录本轮真实产生的输出。
  • 汇总器从最终状态读取完成情况和错误原因。

Bad:

  • packages/app/scripts/run_example.sh 是入口。
  • Session.run_task(...) handles context and scheduled observations.
  • ResultCollector.collect(...) records output status.

Readability Rules

  • A paragraph should still make sense after removing backticked identifiers. If not, rewrite around Chinese actions first.
  • Let Chinese prose carry the concept first, then use source identifiers where they make the mechanism inspectable.
  • Give the reader a concrete handle first: a user action, visible state change, reader concept, or question they already have.
  • 中文技术文档的真人感是具体、直接、敢标未确认;不要为了“有灵魂”加入第一人称、金句或情绪化评价。
  • 成稿前做一次去 AI 表达:删掉“此外”“值得注意”“至关重要”“关键作用”“彰显”等空转词,少用破折号和粗体小标题。
  • 避免让“不仅……而且……”或“不是……而是……”承担主解释;改成正面机制句。
  • Put fields, command shapes, schemas, artifacts, and metrics in the owning module when they are needed to explain the concept.
  • 讲一个机制时,如果读者理解它需要知道输入、状态变化、输出、决策或边界,就先问读者需要看到哪一个最小 case:输入是什么、动作或状态变化是什么、输出/产物是什么、边界或失败分支是什么。缺 case 时不要只补抽象解释;要补可读的代表性输入/输出形状,或明确说明为什么 case 会误导、缺证据或不宜展示。
  • prose 负责解释为什么,结构负责展示是什么。读者需要比较、查字段、看顺序、复现命令或看到输入如何变输出时,优先用 table、列表、时间线、fenced block 或小图;不要把大段连续文字当成质量信号。
  • 按读者问题选择展示策略,而不是按模板固定写法:因果解释用 prose;对比、before/after、字段查找用 table;命令、数据形状、调用形状用 fenced block;生命周期或顺序关系用短时间线 / 有序列表;分支、阶段、状态交接、多路径关系难以用 prose 承载时再用 Mermaid / flowchart。
  • Flowcharts support prose; they do not replace it.
  • A walkthrough step links to the module where a durable concept first matters.
  • 先让 walkthrough 建立行为模型,再让代码地图定位职责。不要用目录树替代主流程解释。

Page Shape Notes

  • README: Chinese opening prose followed by a stable ## 阅读路径 reader-goal table. Use columns 读者目标 | 从这里开始 | 读完后获得什么, including one row that routes evidence audit to references/source-evidence.md.
  • Walkthrough: numbered ## Step N: 行为名 headings; prose explains mechanism; verification appears once near the end.
  • Code map: title 使用“代码地图”。先说明覆盖范围,再用 路径 | 职责 | 关键代码 | 与主流程的关系 汇总;随后按范围内源码目录说明重要文件、功能、关键符号、调用方/使用方和相关验证。目录名本身不能代替中文职责说明,结尾明确列出排除或暂缓区域。
  • Module: use Chinese concept headings shaped by the reader problem. Preserve the module job: concept first, representative case for mechanism-heavy pages, representative source locator later, onward route, and the evidence note. If the reader's easiest path is a question sequence, lifecycle, comparison, timeline, input/output case, or concept-first flow, use that structure.
  • References: fixed generated artifacts only: source-evidence.md and, when needed, quality-review.md. 行为解释放 walkthrough,源码导航放代码地图,机制细节放 modules。

Agent Instruction Block

Use the main repo-docs English Repo docs block for AGENTS.md and other agent instruction Markdown. Chinese mode changes the reader-facing guide language; the agent instruction remains the main repo-docs block.

When a project uses Chinese reader-facing repo-docs/, add one short project-specific routing sentence immediately after the living-guide sentence:

markdown
This repo's `repo-docs/` guide is reader-facing Chinese documentation. When updating reader-facing guide pages, use `repo-docs-zh` when available; keep Chinese reader handles in the prose and preserve exact source identifiers for lookup.

Do not translate the whole agent instruction block into Chinese. The extra sentence only tells future agents to apply this overlay for reader-facing guide pages.

Build Delivery

After first build, reply in Chinese with:

  1. what readers can now understand or follow;
  2. where to start: repo-docs/README.md -> main walkthrough -> repo-docs/code-map.md when locating code;
  3. key pages and their jobs;
  4. scope, uncovered areas, validator result, and root agent-file status.

Keep the reply short. Durable guide history belongs in repo-docs/change-log.md.

Frequently asked questions

What does the Repo Docs Zh AI skill do?

Generate and maintain repo-docs with Chinese as the primary reader language while preserving source identifiers for lookup. Use when the user asks for Chinese repo docs, mentions repo-docs-zh, wants repo documentation in Chinese, or wants an existing repo-docs package localized for Chinese readers.

Why use Repo Docs Zh on TypingMind?

Because you install it once and use it with any model. Repo Docs Zh 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 Repo Docs Zh in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/YurunChen/repo-docs-skills/tree/main/docs/repo-docs-zh. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Repo Docs Zh?

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 Repo Docs Zh?

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

Is the Repo Docs Zh AI skill free?

It is published on GitHub by YurunChen. 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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