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Using Research Writing

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Norman-bury
using-research-writing

Use when starting any research writing task - establishes workflow and routes to appropriate skills

Overview

PublisherNorman-bury
Repositoryresearch-writing-skill
Skill nameusing-research-writing
Stars
3.2K
Forks
214
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 Norman-bury on GitHub. Read the source before you install it.

Installation

Install the Using Research Writing 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.
  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/Norman-bury/research-writing-skill.git /tmp/research-writing-skill
mkdir -p .claude/skills
cp -r /tmp/research-writing-skill/skills/using-research-writing .claude/skills/using-research-writing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Using Research Writing 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 Using Research Writing 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 Using Research Writing 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.

如果你认为有哪怕 1% 的可能性某个技能适用于当前任务,你必须调用该技能。

这不是建议,是强制要求。不允许跳过流程直接写作。不允许找任何借口。

指令优先级

科研写作技能会覆盖默认系统提示行为,但用户指令始终优先

  1. 用户明确指令(直接请求、CLAUDE.md、AGENTS.md 中的设置)— 最高优先级
  2. 科研写作技能 — 覆盖默认系统行为
  3. 默认系统提示 — 最低优先级

如果用户说"不需要讨论直接写",你可以简化流程,但仍需记录到 plan/。

如何访问技能

在 Claude Code 中: 使用 Skill 工具。调用技能时,其内容会被加载并呈现给你 — 直接遵循即可。

在 Cursor 中: 技能通过会话启动 hook 自动加载。使用 Skill 工具调用其他技能。

在 Codex 中: 技能通过符号链接加载。参考 .codex/INSTALL.md

在 OpenCode 中: 使用原生 skill 工具:use skill tool to load research-writing/brainstorming-research

核心规则

在任何响应或行动之前,先调用相关技能。 即使只有 1% 的可能性某个技能适用,你也应该调用它检查。

中型或整篇论文任务必须先调用 paper-orchestration 中型任务包括:影响多个段落、一个以上小节、任一章节、文献论证链、实验/图表设计、或任何已出现质量失败的返工任务。paper-orchestration 负责阶段判断、任务包、子代理分发、两阶段 review 与 capability-use audit(能力使用审计)。

dot
digraph skill_flow {
    "用户消息" [shape=doublecircle];
    "是否论文写作相关?" [shape=diamond];
    "是否中型/整篇任务?" [shape=diamond];
    "调用 paper-orchestration" [shape=box];
    "已完成头脑风暴?" [shape=diamond];
    "调用 brainstorming-research" [shape=box];
    "根据任务调用对应技能" [shape=box];
    "执行任务" [shape=box];
    "更新 plan/progress.md" [shape=box];
    "询问用户确认" [shape=doublecircle];

    "用户消息" -> "是否论文写作相关?";
    "是否论文写作相关?" -> "是否中型/整篇任务?" [label="是"];
    "是否论文写作相关?" -> "执行任务" [label="否,普通问题"];
    "是否中型/整篇任务?" -> "调用 paper-orchestration" [label="是"];
    "是否中型/整篇任务?" -> "已完成头脑风暴?" [label="否"];
    "调用 paper-orchestration" -> "已完成头脑风暴?";
    "已完成头脑风暴?" -> "根据任务调用对应技能" [label="是"];
    "已完成头脑风暴?" -> "调用 brainstorming-research" [label="否"];
    "调用 brainstorming-research" -> "根据任务调用对应技能";
    "根据任务调用对应技能" -> "执行任务";
    "执行任务" -> "更新 plan/progress.md";
    "更新 plan/progress.md" -> "询问用户确认";
}

Red Flags(停止并检查)

这些想法意味着你在找借口 — 停下来:

AI 的想法正确做法
"用户说得很清楚了,直接开始写"必须先完成 brainstorming-research
"这只是修改一小段"检查是否有 plan/,没有则先创建
"先写一段看看效果"必须先确认论文类型和章节结构
"用户很着急,跳过讨论"流程可以加速,但不能跳过关键确认
"这是简单任务,不需要 plan"任何写作任务都需要 plan 记录
"我知道怎么写论文"必须按用户选择的类型和结构写
"先把内容写完再说格式"格式在 brainstorming 阶段确定
"这章内容很简单,不用确认"每章写完都必须让用户确认
"文献我可以补充一些"绝不编造文献,必须可追溯
"我记得这个技能的内容"技能会更新,必须重新读取当前版本

技能路由

任务类型调用技能
中型任务 / 整篇论文 / 多章节协作 / 质量返工paper-orchestration
开始新论文 / 确定选题 / 第一次对话brainstorming-research
引言 / 相关工作 / 背景综述 / 文献驱动段落evidence-driven-writing + literature-review
写某一章节writing-chapters
文献综述literature-review
实验设计 / 结果章节 / mock 数据 / 表格方案experiment-results-planning
画图 / 数据可视化figures-python
流程图 / 架构图figures-diagram
自审 / 检查 / 投稿准备peer-review
统计分析statistical-analysis
LaTeX 输出 / 模板使用latex-output
环境配置 / 安装问题environment-setup
翻译 / 润色 / 去AI化prompts-collection

技能优先级

当多个技能可能适用时,按以下顺序:

  1. 流程技能优先(brainstorming-research)— 决定如何开始任务
  2. 实现技能其次(writing-chapters、literature-review 等)— 指导具体执行

"帮我写论文" → 先 paper-orchestration,再 brainstorming-research,再 writing-chapters "写第三章" → 检查是否已完成 brainstorming,是则直接 writing-chapters

"优化整篇初稿" → 先 paper-orchestration,生成任务包和能力使用审计,再分派章节或图表任务

技能类型

严格型(brainstorming-research、writing-chapters):必须严格遵循,不得跳过步骤。

灵活型(prompts-collection、figures-diagram):可根据上下文调整。

技能本身会说明属于哪种类型。

用户指令

用户指令说的是"做什么",不是"怎么做"。"写第一章"或"帮我润色"不代表跳过工作流。

任务收尾

中型及以上任务完成前必须写入 capability-use audit(能力使用审计),记录应使用的技能、实际使用的技能、已消费资料、未使用资料及原因、产物、验证命令和剩余风险。缺少审计时,不得声称任务完成。

Frequently asked questions

What does the Using Research Writing AI skill do?

Use when starting any research writing task - establishes workflow and routes to appropriate skills

Why use Using Research Writing on TypingMind?

Because you install it once and use it with any model. Using Research Writing 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 Using Research Writing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Norman-bury/research-writing-skill/tree/main/skills/using-research-writing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Using Research Writing?

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 Using Research Writing?

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

Is the Using Research Writing AI skill free?

Yes. It is published on GitHub by Norman-bury 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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