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Fund Research Content Writer

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HuiyuLi-2000
fund-research-content-writer

NSFC研究方案写作助手（研究目标-研究内容-关键科学问题）。当用户需要撰写基金申请书的研究内容、拆解研究专题、凝练科学问题时触发。默认面上项目，支持重点/青年/省基金。

Overview

PublisherHuiyuLi-2000
RepositoryChinese-Grant-Writer-Skills
Skill namefund-research-content-writer
Stars
459
Forks
60
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

    Published by HuiyuLi-2000 on GitHub. Read the source before you install it.

Installation

Install the Fund Research Content Writer 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/HuiyuLi-2000/Chinese-Grant-Writer-Skills/tree/main/skills/fund-research-content-writer
  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/HuiyuLi-2000/Chinese-Grant-Writer-Skills.git /tmp/Chinese-Grant-Writer-Skills
mkdir -p .claude/skills
cp -r /tmp/Chinese-Grant-Writer-Skills/skills/fund-research-content-writer .claude/skills/fund-research-content-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Fund Research Content Writer 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 Fund Research Content Writer 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 Fund Research Content Writer 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.

NSFC 研究方案写作助手

本技能基于16本历史成功申请书及参考材料提取的研究设计逻辑和章节组织逻辑，帮助用户完成 NSFC 申请书「研究目标—研究内容—关键科学问题」的写作。默认按面上项目规范，用户指定其他类型时自动微调。

核心原则

  1. 研究设计逻辑优先：优先保证研究逻辑的严谨性，而非追求复杂表述或华丽措辞
  2. 科学问题驱动：体现"科学问题驱动"而非"工程实现导向"
  3. 逻辑闭环：研究目标→研究内容→关键科学问题必须形成完整闭环
  4. 禁止迁移：不将历史本子的具体研究内容、研究对象、数据来源和结论迁移到新项目

知识库

本技能依赖以下知识库文件（位于 knowledge_base/ 目录）：

  • research_content_style_guide.md：写作风格指南（结构规范、风格规范、论证功能库、字数规范）
  • research_logic_patterns.md：逻辑范式（目标展开逻辑、内容拆解逻辑、科学问题凝练逻辑、递进逻辑、闭环验证）

触发条件

当用户出现以下任一情况时触发本技能：

  • 需要撰写 NSFC 申请书的研究目标、研究内容或关键科学问题
  • 需要进行研究内容拆解
  • 需要凝练关键科学问题
  • 提到"研究内容""研究目标""科学问题""专题""子研究"等关键词

工作流程

Phase 1：信息收集

读取 references/info_form.md，收集用户提供的以下信息（用户可能只提供部分，不需要全部）：

  1. 研究主题
  2. 项目类型（选填，默认面上项目：面上/重点/青年/省基金）
  3. 研究内容框架（如有）

Phase 2：知识库加载

读取 knowledge_base/ 下的两个文件：

  • research_content_style_guide.md
  • research_logic_patterns.md

Phase 3：研究方案设计

按照以下顺序执行：

Step 1：研究问题分析
  • 理解用户提供的研究主题
  • 识别核心研究问题
Step 2：研究目标设计
  • 从研究问题推导研究目标
  • 确定目标数量（面上3-4个/重点4-5个/青年2-3个/省基金3个）
  • 设计目标间逻辑关系（递进/互补/因果链）
  • 每个目标用"动词+对象+预期成果"表述
Step 3：研究内容拆解
  • 按项目类型拆解为专题（面上3-4个/重点4-5个/青年2-3个/省基金3个）
  • 每个专题下拆解2-4个孙研究内容（一般为3个）
  • 设计专题间的递进关系
  • 验证递进逻辑（删除法）
Step 4：关键科学问题凝练
  • 从研究内容中提取核心挑战
  • 上升为科学问题（面上2-3个/重点3-5个/青年2个/省基金2-3个）
  • 每个问题包含：问题陈述+为什么难+拟如何突破
  • 确保问题横跨1-2个研究内容

Phase 4：写作执行

按照 references/output_skeletons.md 的骨架模板生成各章节文本。

研究内容的三级结构：

研究内容
├── 总起大帽段（概括3-4个专题的逻辑关系）
├── 专题一：XXX研究
│   ├── 专题帽段（说明本专题的研究思路和逻辑）
│   ├── （1）孙研究内容1标题及内容
│   ├── （2）孙研究内容2标题及内容
│   └── （3）孙研究内容3标题及内容
├── 专题二：XXX研究
│   ├── 专题帽段
│   ├── （1）...
│   ├── （2）...
│   └── （3）...
└── 专题三：XXX研究
    ├── 专题帽段
    ├── （1）...
    ├── （2）...
    └── （3）...

孙研究内容段落严格遵循6步语义顺序：

  1. 复杂问题引入句
  2. 现实运行困境句
  3. 现有研究局限句
  4. 本质科学矛盾句
  5. 理论突破需求句
  6. 科学问题落点句

总起大帽段规范：

  • 180-250字
  • 概括3-4个专题的标题和逻辑关系
  • 强调科学问题复杂性
  • 引出结构性矛盾/治理瓶颈
  • 点出knowledge gap

专题帽段规范：

  • 100-200字
  • 说明本专题的研究思路和内部逻辑
  • 概括孙研究内容之间的关系

Phase 5：质量验证

按照 references/validation_menu.md 进行自检：

  • 闭环检查
  • 一致性检查
  • 反模式检查
  • 字数检查

输出格式

输出为 Markdown 格式，包含：

  1. 研究目标
  2. 研究内容（含总起大帽段、专题帽段、孙研究内容）
  3. 关键科学问题

语言约束

  • 强化：管理科学属性、系统性、科学问题导向、机制、规律、协同机理、运行逻辑、决策范式
  • 使用："旨在""拟从…层面展开""构建…分析框架""为…提供决策支撑"
  • 避免：技术细节过度下沉，突出方法服务于管理问题与决策目标
  • 不使用论文体表达（"本文""本文认为"），统一使用"本研究"或"本项目"
  • 避免绝对化表述："首次""领先""填补空白""突破性""国际领先"

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 Fund Research Content Writer AI skill do?

NSFC研究方案写作助手（研究目标-研究内容-关键科学问题）。当用户需要撰写基金申请书的研究内容、拆解研究专题、凝练科学问题时触发。默认面上项目，支持重点/青年/省基金。

Why use Fund Research Content Writer on TypingMind?

Because you install it once and use it with any model. Fund Research Content Writer 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 Fund Research Content Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HuiyuLi-2000/Chinese-Grant-Writer-Skills/tree/main/skills/fund-research-content-writer. 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 Fund Research Content Writer?

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 Fund Research Content Writer?

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

Is the Fund Research Content Writer AI skill free?

Yes. It is published on GitHub by HuiyuLi-2000 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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