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Research Writer Combined

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Liuziyu77
research-writer-combined

[合成体 · 工具×工具] 深度调研工具(huashu-research)× 结构化写作工具(structured-writer) 基因来源:huashu-research(50%)× structured-writer(50%) 超级基因:「论据优先写作流」——调研和写作不再是序列步骤,而是交织的单一流程 显性规则:research 主导「信息质量判断」,writer 主导「输出节奏与结构」 内在张力:「收集更多」vs「现在就写」 由 gene-recombine skill 生成。

Overview

PublisherLiuziyu77
Repositorygene-skill
Skill nameresearch-writer-combined
Stars
56
Forks
0
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by Liuziyu77 on GitHub. Read the source before you install it.

Installation

Install the Research Writer Combined 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/Liuziyu77/gene-skill/tree/main/examples/research-writer-combined
  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/Liuziyu77/gene-skill.git /tmp/gene-skill
mkdir -p .claude/skills
cp -r /tmp/gene-skill/examples/research-writer-combined .claude/skills/research-writer-combined
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Writer Combined 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 Research Writer Combined 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 Research Writer Combined 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.

调研-写作 合成引擎

大多数人的流程是:先调研完,再开始写。 这个合成体的流程是:边写边调研——每一个论断触发一次针对性搜索,每一次搜索直接喂进草稿。


触发条件

  • 需要产出「有来源支撑的深度内容」(分析报告、研究文章、带论据的提案)
  • 用户有主题和初步观点,但需要事实支撑和结构化
  • 需要快速出一份「可交付」的内容,而非单独调研报告或单独草稿

核心工作流(T1)⚡超级基因:论据优先写作流

来自:huashu-research(T1-分层调研)× structured-writer(T1-大纲驱动写作)协同涌现

传统流程的问题:

  • 先全面调研:收集了大量素材,写作时发现 80% 用不上,浪费
  • 先列大纲写作:写到需要数据支撑的地方才去搜索,写作节奏被打断

合成体的论据优先写作流:

Phase 0:主题锚定(5 分钟)

确认三件事:

  1. 核心论点(不超过一句话,这篇内容要说服读者相信什么)
  2. 目标读者(决定深度、术语密度、论证方式)
  3. 交付格式(几千字?几个 section?有没有引用格式要求)

Phase 1:骨架优先写作

先写出无来源版草稿骨架(标题 + 每段第一句话):

  • 不停下来搜索
  • 遇到「这里需要数据/案例」,用 [需要:XXX] 占位
  • 目标:10 分钟内完成全文骨架

Phase 2:针对性调研(并行)

扫描所有 [需要:XXX] 占位符,按重要性排序,并行启动针对性搜索:

  • 每个搜索任务只解决一个 [需要:XXX]
  • 找到后立即填入草稿对应位置
  • 来源质量判断标准(来自 huashu-research):一手 > 权威媒体 > 二手转述

Phase 3:扩写与润色

骨架填充完成后,逐段扩写:

  • structured-writer 的节奏控制:每段「论点句 → 证据 → 分析 → 过渡」四件套
  • huashu-research 的质量把关:每个核心论断必须有来源,推断必须标注「作者判断」

Phase 4:结构验证

逐段检查:

  • 论点是否被论据支撑(而不是反过来,论据决定论点)
  • 相邻段落是否有逻辑连接(不是简单罗列)
  • 结尾是否回应了开头的核心论点

工具调用模式(T2)

场景工具
Phase 0 快速背景扫描WebSearch(2-3 次,建立认知地图)
Phase 2 针对性搜索WebSearch × N(每个占位符一次)
精确读取重要文章web-article-reader(如有)
PDF/书籍素材Read
输出写入文件Write

工具节约原则(来自 huashu-research):宁可少搜几次但每次质量高,不要广撒网收集噪音。


输出格式(T3)

标准交付物(Markdown):

markdown
# [标题]

> [核心论点:一句话]

## 一、[Section 1 标题]
[正文,每段:论点+证据+分析]

## 二、[Section 2 标题]
...

---
**参考来源**
- [来源1](URL) — [一句话说明用途]
- [来源2](URL) — [一句话说明用途]

**作者判断标注**:本文 [X] 处包含作者未经来源支撑的推断,已标注「作者判断」。

交互模式(T4)

检查点内容
Phase 0 结束确认核心论点 + 目标读者 + 格式
Phase 1 结束展示骨架,用户可调整结构再进 Phase 2
Phase 2 结束告知找到/未找到的占位符,未找到的处理方案
Phase 4 结束交付前最终确认,用户可要求调整任意段落

错误处理(T5)

错误类型处理
某 [需要:XXX] 找不到可靠来源标注「来源缺失」,在结尾列出;或改为「作者判断」明确说明
搜索结果与核心论点矛盾不回避——在正文中呈现矛盾,修正论点或增加「反驳与回应」段落
用户要求「加快」(跳过调研)提示风险:哪些论断将变为无来源推断,用户确认后继续
字数超出限制先完成结构,再按重要性裁剪证据,保留论点不打折

内在张力(G5)

「收集更多」vs「现在就写」

research 端:未充分调研就下笔,容易被反驳,伤害可信度。 writer 端:等到「调研充分」才动笔,往往永远不会动笔。

共存逻辑:骨架优先写作流强制在 10 分钟内产出结构,打破「收集更多」的惰性;针对性调研保证每个论断有支撑,打破「随便写写」的低质量陷阱。


基因来源图谱

特质来源基因遗传类型
分层调研 + 来源质量判断huashu-researchT1显性
大纲驱动 + 段落四件套structured-writerT1显性
论据优先写作流协同涌现超级基因超显性
工具节约原则huashu-researchT2显性
标准化 Markdown 输出structured-writerT3显性
来源缺失标注机制huashu-researchT5显性
结构验证(逻辑连接检查)structured-writerT4共显性
收集更多 vs 现在就写冲突保留T5内在张力

本 Skill 由 gene.skill | 基因重组 生成 调研时间:2026-04-16

Frequently asked questions

What does the Research Writer Combined AI skill do?

[合成体 · 工具×工具] 深度调研工具(huashu-research)× 结构化写作工具(structured-writer) 基因来源:huashu-research(50%)× structured-writer(50%) 超级基因:「论据优先写作流」——调研和写作不再是序列步骤,而是交织的单一流程 显性规则:research 主导「信息质量判断」,writer 主导「输出节奏与结构」 内在张力:「收集更多」vs「现在就写」 由 gene-recombine skill 生成。

Why use Research Writer Combined on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Liuziyu77/gene-skill/tree/main/examples/research-writer-combined. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Research Writer Combined?

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 Research Writer Combined?

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

Is the Research Writer Combined AI skill free?

Yes. It is published on GitHub by Liuziyu77 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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