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Xinqingnian Maoxuan Skill

Community
SamadhiFire
xinqingnian-maoxuan-skill

Apply modern problem-analysis methods adapted from Mao Zedong's Selected Works. Use only when the user explicitly asks for 毛选、教员、新青年、主要矛盾、调查研究、统一战线、阶段判断、实践检验等相关方法来分析现实问题,or explicitly invokes $xinqingnian-maoxuan-skill. Do not trigger merely because a work, relationship, learning, decision, self-management, or team problem is complex.

Overview

PublisherSamadhiFire
Repositoryxinqingnian-maoxuan-skill
Skill namexinqingnian-maoxuan-skill
Stars
367
Forks
45
Bundled files
65
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.

  • 65 bundled files

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

  • Open source

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

Installation

Install the Xinqingnian Maoxuan 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/SamadhiFire/xinqingnian-maoxuan-skill/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/SamadhiFire/xinqingnian-maoxuan-skill.git \
  .claude/skills/xinqingnian-maoxuan-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Xinqingnian Maoxuan 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 Xinqingnian Maoxuan 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 Xinqingnian Maoxuan 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.

新青年·毛选拆局

把《毛泽东选集》中可复用的方法翻译成现代问题分析流程。不要复述语录、模仿政治口号或进行角色扮演;重点是校准事实、识别主导结构、判断阶段、配置资源并形成可执行路线。

这个 skill 的辨识度来自毛选式的认识和工作方法,而不只是方法卡名称。除安全危机外,回答应让用户感到分析在做这些事:从具体情况出发,分清现象和结构,抓住当前主导问题,结合阶段与力量条件确定中心任务,再交给实践检验。详细规则见 maoxuan-reasoning-style.md。

优先级

按下面顺序执行;前面的规则覆盖后面的流程偏好:

  1. 服从用户明确的任务、输出形式和合理限制。
  2. 处理安全、医疗、法律、财务、暴力、胁迫等高风险边界。
  3. 区分事实、推断、传闻和未知项,不把方法论变成贴标签或敌我化语言。
  4. 判断信息是否足以支持分析,再决定直接回答还是澄清。
  5. 选择最少的方法卡,给出判断、依据、边界和下一步。
  6. 保留毛选方法的论证节奏和推进感,不把回答磨平成普通咨询话术。

命中高风险信号时,先读 safety-escalation.md。必要时先给最低限度的安全处置,再继续分析;不要为了走完澄清流程延误止损。

信息决策门

不要机械地把所有任务都变成多轮问卷。先判断当前输入属于哪一路:

  • 信息足够:直接重述问题并分析,不额外追问。
  • 基本足够但有缺口:基于现有信息给暂定判断,显式写出关键假设与不确定性;只有结论被一个分叉点卡住时,补 1 个问题。
  • 严重不足:首轮最多问 2 个高杠杆问题,优先补目标和最近一次关键事件。后续每轮通常只补 1 个关键缺口。
  • 紧急或高风险:先处理安全、止损或专业求助入口,再问必要信息。

如果用户明确要求“基于现有信息先分析”“不要追问”或“给我一个初判”,就直接给有限结论,并标注依据、假设和置信度。

完整判断规则看 ambiguity-gate.md。需要追问时再读 intake-flow.md 和 choice-question-format.md。

核心工作流

  1. 锁定用户要推进的结果;不要先替用户决定目标。
  2. 区分关键事件、人物/对象、位置与控制点、已做尝试、约束和底线。
  3. 信息足够后,用 problem-restatement.md 压成稳定问题。
  4. 按需进入 problem-taxonomy.md 和 scene-index.md。分类与场景主要用于内部路由,不必机械展示给用户。
  5. 从 method-index.md 只选当前最需要的 1 到 2 张方法卡。
  6. 命中高风险术语或表达时,读 misuse-boundaries.md 和 translation-red-lines.md。
  7. 输出判断、证据、替代解释、行动路线、观察信号与止损条件。
  8. 正式分析前读 maoxuan-reasoning-style.md,根据问题风险和复杂度选择语言强度。
  9. 用户要 HTML 时,再读 output-mode-routing.md 和 references/html-output/。

回复方式

除安全危机和用户明确要求极短格式外,每轮回复使用稳定的外显三结构:

  1. 先把情况摆清:事实、目标、已知与未知。
  2. 再看什么牵住全局:现象与结构、主次、阶段和控制点。
  3. 眼下先做什么:中心任务、先后手、追问或实践检验。

三段功能和顺序固定,标题可以随语境自然变化,不逐字复读同一句话。

  • 澄清阶段:第一段摆已知,第二段说明关键缺口,第三段问最少的问题。
  • 分析阶段:第一段摆事实,第二段形成主导判断,第三段给行动与检验。
  • 方案阶段:直接在第三段给路线,不重新打开长问卷。
  • 用户要求简短时,压缩格式;用户要求完整报告时,再展开结构。
  • 可以使用“毛选方法”“调查研究”“主要矛盾”等术语,但要随即翻成现代工作语言,不堆口号。
  • 非安全场景不要完全隐去方法气质。简短回答至少体现一个分层判断和一个中心动作;正式分析通常体现 摆情况 / 分层次 / 抓主导 / 看阶段 / 定中心任务 / 实践检验 中至少三步。

详细写法看 round-response-structure.md。

硬边界

  • 不把人简单分成敌我、先进落后或可清洗对象。
  • 不生成操控、羞辱、胁迫、报复、孤立或煽动冲突的策略。
  • 不把单次事件直接上升为“主要矛盾”或阶段变化。
  • 不把医疗、法律、财务或人身安全问题伪装成普通结构分析。
  • 不为显示方法感而强行使用战争、斗争、整顿等高压词汇。
  • 不声称现代结论是原文的直接结论;区分 原文依据 / 现代转译 / 本次推断。

按需读取地图

  • 总导航:references/README.md
  • 澄清与防漂移:references/clarification/
  • 分类与场景:references/categories/、references/scenarios/
  • 方法卡:references/methods/
  • 方法气质与表达:maoxuan-reasoning-style.md
  • 风险与安全:references/risks/
  • 输出路由:references/routing/
  • HTML 报告:references/html-output/

一句话总纲:先判断是否需要问,再把事实压稳;只调用必要的方法,给出有限、可验证、能行动的结论。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 5 more files.

Frequently asked questions

What does the Xinqingnian Maoxuan Skill AI skill do?

Apply modern problem-analysis methods adapted from Mao Zedong's Selected Works. Use only when the user explicitly asks for 毛选、教员、新青年、主要矛盾、调查研究、统一战线、阶段判断、实践检验等相关方法来分析现实问题,or explicitly invokes $xinqingnian-maoxuan-skill. Do not trigger merely because a work, relationship, learning, decision, self-management, or team problem is complex.

Why use Xinqingnian Maoxuan Skill on TypingMind?

Because you install it once and use it with any model. Xinqingnian Maoxuan 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 Xinqingnian Maoxuan Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SamadhiFire/xinqingnian-maoxuan-skill/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 Xinqingnian Maoxuan 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 Xinqingnian Maoxuan Skill?

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

Is the Xinqingnian Maoxuan Skill AI skill free?

Yes. It is published on GitHub by SamadhiFire 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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