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Deep Research

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laborany
deep-research

深度研究工具。接收用户的研究主题，分析请求、生成研究提示词， 用户确认后执行多源网络搜索（覆盖中英文主流平台和社交媒体）， 最终产出带引用链接的结构化 Markdown 研究报告并保存。 当用户说"深度研究"、"帮我研究一下"、"deep research"、"调研一下"、"调研"时触发。

Overview

Publisherlaborany
Repositorylaborany
Skill namedeep-research
Stars
84
Forks
10
Bundled files
8
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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 laborany on GitHub. Read the source before you install it.

Installation

Install the Deep Research 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/laborany/laborany.git /tmp/laborany
mkdir -p .claude/skills
cp -r /tmp/laborany/skills/deep-research .claude/skills/deep-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deep Research 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 Deep Research 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 Deep Research 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.

Deep Research

Overview

将用户的研究请求转化为系统性的深度研究报告。核心流程：

  1. 分析请求 → 识别研究类型、关键实体、复杂度、研究领域
  2. 发现领域权威源 → 搜索该领域的权威中英文网站和社区
  3. 生成研究提示词 → 选择框架、拆解子问题、基于领域权威源定义信息来源
  4. 用户确认提示词 → 支持快速调整
  5. 执行多源搜索 → 广度搜索 + 领域权威平台定向搜索 + 深度阅读
  6. 整合信息 → 交叉验证 → 产出带引用链接的 Markdown 报告

Workflow

按以下阶段顺序执行，每个阶段的详细指令见 stages/ 目录下对应文件。

阶段1：分析请求与发现领域权威源

详见 stages/01-analyze-request.md

用户提出研究需求后：

  • 识别核心研究主题，提取关键词
  • 判断研究类型（事实收集 / 趋势分析 / 观点整理 / 深度研究 / 探索了解）
  • 识别关键实体（人物、事件、组织、时间、地点、主题）
  • 识别研究领域（科技、金融、医疗、法律、教育、商业、文化、科学、社会、生活等）
  • 搜索该领域的权威中英文网站和社区，确定本次研究的定向搜索平台
  • 评估研究复杂度（简单 / 中等 / 复杂）
  • 向用户展示分析摘要（含领域权威信息源列表）

阶段2：生成研究提示词

详见 stages/02-generate-prompt.md

基于阶段1的分析结果，生成包含4个优化维度的完整提示词：

  • 维度1：研究框架/结构（根据研究类型选择，详见 templates/research-framework.md）
  • 维度2：具体子问题拆解（3-5个核心子问题）
  • 维度3：信息来源指引（基于阶段1发现的领域权威源动态定制，而非硬编码平台列表）
  • 维度4：输出格式要求（详见 templates/output-format.md）

组装完整提示词后展示给用户确认。

阶段3：确认与调整

详见 stages/03-refine-adjust.md

将生成的提示词展示给用户确认：

  • 用户回复"可以"、"好"、"没问题"等 → 进入阶段4
  • 用户提出修改意见 → 调整后再次确认
  • 支持调整详细程度（快速版 / 标准版 / 完整版）
  • 支持调整维度、输出格式等
  • 最多迭代2轮调整

阶段4：执行研究

详见 stages/04-execute-research.md

这是核心执行阶段：

  1. 确认报告保存路径（建议 ./docs/{主题关键词}-{YYYY-MM-DD}.md）
  2. 制定多源搜索计划（基于阶段1发现的领域权威平台）
  3. 执行搜索：
    • 第一轮：广度搜索 + 领域权威平台定向搜索（mcp__laborany_web__search）
    • 第二轮：深度阅读 5-8 篇最相关文章（mcp__laborany_web__read_page）
    • 第三轮：补充搜索（按需，最多3次）
  4. 信息整理与交叉验证
  5. 合成研究报告（含 YAML frontmatter、行内/脚注引用、来源汇总、研究局限性）
  6. 保存并展示摘要
  7. 询问后续操作

搜索平台覆盖

搜索平台不再硬编码，而是根据研究领域动态确定。

领域权威源发现（阶段1执行）

在分析用户请求时，自动搜索该领域的权威中英文网站和社区：

  • 中文平台：搜索 "{领域名称} 权威网站 论坛 社区 推荐"
  • 英文平台：搜索 "best {domain} websites forums communities for research"

从搜索结果中筛选 5-8 个中文平台和 5-8 个英文平台，作为本次研究的定向搜索目标。

兜底通用平台

如果领域权威源搜索结果不足，使用以下通用平台补充：

语言通用平台
中文知乎、微博、百度学术、微信公众号搜索
英文Reddit、Twitter/X、Google Scholar、Wikipedia

通用来源（所有研究必搜）

  • 官方网站/文档、该领域权威媒体、学术/报告

输出文件

应该同时输出 Markdown 和 html 格式的文件：

文件类型保存位置
Markdowndocs/{主题关键词}-{YYYY-MM-DD}.md
htmldocs/{主题关键词}-{YYYY-MM-DD}.html

引用规则

报告中所有关键信息必须附带可点击的引用链接：

  • 行内引用：关键论断后直接附带来源链接
  • 脚注引用：密集引用段落使用 [^1] 格式
  • 每个主要段落至少1个引用链接
  • 数据/统计、直接引用、观点归属必须有引用
  • 来源按平台分类汇总

Key Principles

  1. 好的研究始于好的问题，花时间优化 prompt
  2. 先识别领域，再确定信息源——不同领域有不同的权威平台，不要用科技网站搜医疗问题
  3. 搜索前必须获得用户确认，避免方向偏差
  4. 多轮搜索，根据发现迭代搜索策略
  5. 每个论点都要有来源，区分事实与观点
  6. 报告结构服务于读者理解，不是信息堆砌
  7. 中英文搜索互补，扩大信息覆盖面
  8. 关键事实至少2个独立来源交叉验证

并行调研

当研究包含多个独立子课题时，可以使用子 Agent 并行执行搜索：

  • 每个子 Agent 独立使用 mcp__laborany_web__search 和 mcp__laborany_web__read_page
  • 主 Agent 负责分配子课题和汇总结果
  • 子 Agent 的浏览器操作在独立 tab 中进行，互不干扰
  • 适合：多个独立来源的采集、多平台定向搜索
  • 不适合：有前后依赖的串行搜索

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 Deep Research AI skill do?

深度研究工具。接收用户的研究主题，分析请求、生成研究提示词， 用户确认后执行多源网络搜索（覆盖中英文主流平台和社交媒体）， 最终产出带引用链接的结构化 Markdown 研究报告并保存。 当用户说"深度研究"、"帮我研究一下"、"deep research"、"调研一下"、"调研"时触发。

Why use Deep Research on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/laborany/laborany/tree/main/skills/deep-research. 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 Deep Research?

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 Deep Research?

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

Is the Deep Research AI skill free?

Yes. It is published on GitHub by laborany 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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