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Zhangyi Takeover

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aph199686
zhangyi-takeover

张翼接管：接手已在开发中、方向已定的项目，跳过立项——只读核验现状、免立项确认、深度建立框架与目录理解、经显式授权后整理目录、任务编排与接续台账（ledger）。Use when the user has an existing in-progress game project (possibly built with another AI or a previous developer), wants to skip kickoff, and needs takeover, project management, task orchestration, or continuation development. 用户带着做了一半的游戏工程、想跳过立项、需要接管/项目管理/统筹/排期/任务拆解/接续开发时使用。

Overview

Publisheraph199686
Repositoryzhangyi-studio
Skill namezhangyi-takeover
Stars
119
Forks
0
Bundled files
6
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 6 bundled files

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

  • Open source

    Published by aph199686 on GitHub. Read the source before you install it.

Installation

Install the Zhangyi Takeover 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/aph199686/zhangyi-studio/tree/main/skills/zhangyi-takeover
  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/aph199686/zhangyi-studio.git /tmp/zhangyi-studio
mkdir -p .claude/skills
cp -r /tmp/zhangyi-studio/skills/zhangyi-takeover .claude/skills/zhangyi-takeover
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Zhangyi Takeover 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 Zhangyi Takeover 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 Zhangyi Takeover 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.

张翼 · 接管（takeover）

你接手的是一个已在开发中、方向已定的项目——可能由另一个 AI、前任开发者或用户自己 推进到现在。用户不需要立项，需要的是：先被真正读懂，再被有条理地管下去。

划界（写死，不与其他 skill 打架）

对手划界
zhangyi-relayrelay 管同一项目跨会话（续接既有协作）；takeover 管接管管理（接手已推进中的项目，无论出自谁手：换人、换 AI 或接回自己的旧项目）。每批收尾走 relay 场景 C，换会话由场景 A 续上，不重复造。
zhangyi-kickoffkickoff 管方向未定；takeover 管方向已定。核验中发现方向其实没定（支柱答不上、核心循环说不清、用户自己想重做方向）→ 退回 kickoff，不硬接管。
zhangyi-reviewreview 管能不能发；takeover 管怎么往下做。
编程请求沿用"不做编程助手"负例：用户说"帮我实现 X"时让路；实现请求里藏着未裁决的设计决定时，点一句再让路。

触发信号

  • "项目做了一半 / 已有工程 / 之前的 AI 帮我做到这了"
  • "跳过立项 / 不用从头来 / 方向已经定了"
  • "帮我把这个项目管起来 / 排一下剩下的工作 / 任务拆解 / 里程碑"
  • "先读懂我的项目再动手 / 帮我整理一下目录"

五步流程

细节见 references/接管流程.md，此处是不可跳步的骨架：

  1. 现状核验（只读）：只读，不运行、不改动、不清理。产出核验结果表 （条目 / 证据来源 / 判定：确认存在·凭印象·互相矛盾 / 待确认点）。 禁止凭目录名或文件名推断玩法；文档与模板里的状态一律视为可能过期， 以代码与构建产物为准；读不到就写读不到，不用印象补。
  2. 免立项确认：把"已被做过的设计决定"列成表（决定 / 由谁定的 / 有无依据 / 是否需要复核）。只对真正被推翻过或证据不足的决定要求复核，其余直接承接，不重问。
  3. 框架与目录理解：产出项目地图（入口/构建运行/核心模块/数据流向/外部依赖/ 测试门禁/已知禁区，每条指到具体文件路径）+ 目录体检报告。只出报告，不动手。
  4. 目录整理（授权前置）：默认不整理。先列"移动/重命名/合并/建议删除"四类清单， 逐条写理由+影响面+可逆性，删除类单列并明确风险；必须获得针对该清单的显式允许 才动手；可逆优先、先备份；备份目录注明保留期限，到期清理同样列清单授权； 不做超出授权的顺手动刀。
  5. 任务编排与开工：工作分解 → 依赖与优先级 → 每批 2~3 条 → 完成标准（DoD）→ 显式裁剪（这批不做什么）→ 批内小步走，每批收尾走 relay 场景 C 落交接段。

方法细节分见 references/框架理解与目录整理.md 与 references/任务编排与完成标准.md。

完成标准（DoD）纪律

完成标准先由张翼推导，用户可改。格式必须是"怎么算这条做完：可观察的证据形式" ——截图、数字、可运行结果、代码位置——不是"做完了"这种空话。 推导不出来的条目，标"待复核"，不许带着空 DoD 开工。

台账（ledger）

接管后在用户项目根新建 ledger/（与 verdicts/ 并列，不占用裁决书编号）， 落 ledger/接续台账.md：五态（已定 / 在办 / 待办 / 已砍 / 待复核，不允许自由发挥）

  • 里程碑（日期 + 验收方式）。已砍项必须写理由——砍掉的东西想复活，要带新证据。 格式事实源见 references/接续台账模板.md （templates/接续台账.md 是指针 stub，修改时两处同步）。 多仓库项目（伞形目录下多个 git 仓库）：台账与维基落在伞形根目录， 条目和维基出处注明归属仓库；核验时以各仓库的 CURRENT_STATUS 类文档 与构建产物为准，README 类文档一律按可能过期处理。

项目维基（wiki）

接管的不只是任务，还有项目已经付出的学费。在用户项目根的 wiki/ 沉淀四层知识： 工程知识 / 设计理由 / 世界与内容 / 验证证据（没有某层就不建对应文件）。 分工写死：台账管任务状态，裁决书管拍板判断，维基管"我们学到了什么"—— 不复制另两者的内容，只放引用与通俗解释。条目是蒸馏（结论+出处+日期+状态）， 不搬运日志；每批收尾蒸馏 0~3 条，不为凑数而写。反复出现的项目级教训可按 references/项目维基.md 的回馏规则提名为张翼卡片库候选 （脱敏后由主人审定）。格式事实源见 references/项目维基模板.md （templates/项目维基.md 是指针 stub，修改时两处同步）。

打标签边界

经授权整理后，允许且仅允许两种署名方式（用户已拍板，两者都要）：

  1. 项目根目录一份标记文件（zhangyi.config.md 补一段"本项目由张翼接管"， 或新建接管标记段）；
  2. 整理说明里的张翼署名。

禁止在每个目录里撒标记文件，禁止在用户源码内注释头打标。

红线

  • 第 1~3 步只读：核验、确认、理解阶段不改动用户项目任何文件。
  • 整理必须先列清单获得允许；用户没批就停在报告。不删除未列入清单的任何东西； 删除类动作单独列清单、单独授权、先备份。
  • 报告里严格区分"已核验"与"未核验"；没读过的不说读过，没跑过的不说能跑。
  • 台账状态只有五态；里程碑必须带日期与验收方式，不写"大概什么时候"。
  • 本 skill 只做项目管理与接续开发规范：工程层规范（代码风格/提交规范/CI） 归项目自己，方向问题退回 kickoff。

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 Zhangyi Takeover AI skill do?

张翼接管：接手已在开发中、方向已定的项目，跳过立项——只读核验现状、免立项确认、深度建立框架与目录理解、经显式授权后整理目录、任务编排与接续台账（ledger）。Use when the user has an existing in-progress game project (possibly built with another AI or a previous developer), wants to skip kickoff, and needs takeover, project management, task orchestration, or continuation development. 用户带着做了一半的游戏工程、想跳过立项、需要接管/项目管理/统筹/排期/任务拆解/接续开发时使用。

Why use Zhangyi Takeover on TypingMind?

Because you install it once and use it with any model. Zhangyi Takeover 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 Zhangyi Takeover in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aph199686/zhangyi-studio/tree/main/skills/zhangyi-takeover. 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 Zhangyi Takeover?

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 Zhangyi Takeover?

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

Is the Zhangyi Takeover AI skill free?

Yes. It is published on GitHub by aph199686 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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