Research Deep

CommunityPopular

Weizhena

research-deep

读取调研outline，为每个item启动独立agent进行深度调研。禁用task output。

Overview

Publisher	Weizhena
Repository	`Deep-Research-skills`
Skill name	`research-deep`
Stars	2.2K
Forks	176
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 Weizhena on GitHub. Read the source before you install it.

Installation

Install the Research Deep AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/Weizhena/Deep-Research-skills/tree/master/skills/research-zh/research-deep

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/Weizhena/Deep-Research-skills.git /tmp/Deep-Research-skills
mkdir -p .claude/skills
cp -r /tmp/Deep-Research-skills/skills/research-zh/research-deep .claude/skills/research-deep

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Research Deep 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 Deep 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 Deep 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.

Research Deep - 深度调研

触发方式

/research-deep

执行流程

Step 1: 自动定位Outline

在当前工作目录查找 */outline.yaml 文件，读取items列表、execution配置（含items_per_agent）。

Step 2: 断点续传检查

检查output_dir下已完成的JSON文件
跳过已完成的items

Step 3: 分批执行

按batch_size分批（完成一批需要得到用户同意才可进行下一批）
每个agent负责items_per_agent个项目
启动web-search-agent（后台并行，禁用task output）

参数获取：

{topic}: outline.yaml中的topic字段
{item_name}: item的name字段
{item_related_info}: item的完整yaml内容（name + category + description等）
{output_dir}: outline.yaml中execution.output_dir（默认./results）
{fields_path}: {topic}/fields.yaml的绝对路径
{output_path}: {output_dir}/{item_name_slug}.json的绝对路径（slugify处理item_name：空格替换为_，移除特殊字符）

硬约束：以下prompt必须严格复述，仅替换{xxx}中的变量，禁止改写结构或措辞。

Prompt模板：

python
prompt = f"""## 任务
调研 {item_related_info}，输出结构化JSON到 {output_path}

## 字段定义
读取 {fields_path} 获取所有字段定义

## 输出要求
1. 按fields.yaml定义的字段输出JSON
2. 不确定的字段值标注[不确定]
3. JSON末尾添加uncertain数组，列出所有不确定的字段名
4. 所有字段值必须使用中文输出（调研过程可用英文，但最终JSON值为中文）

## 输出路径
{output_path}

## 验证
完成JSON输出后，运行验证脚本确保字段完整覆盖：
python ~/.claude/skills/research/validate_json.py -f {fields_path} -j {output_path}
验证通过后才算完成任务。
"""

One-shot示例（假设调研GitHub Copilot）：


## 任务
调研 name: GitHub Copilot
category: 国际产品
description: Microsoft/GitHub开发，首个主流AI编程助手，市场份额约40%，输出结构化JSON到 {project_dir}/results/GitHub_Copilot.json

## 字段定义
读取 {project_dir}/fields.yaml 获取所有字段定义

## 输出要求
1. 按fields.yaml定义的字段输出JSON
2. 不确定的字段值标注[不确定]
3. JSON末尾添加uncertain数组，列出所有不确定的字段名
4. 所有字段值必须使用中文输出（调研过程可用英文，但最终JSON值为中文）

## 输出路径
{project_dir}/results/GitHub_Copilot.json

## 验证
完成JSON输出后，运行验证脚本确保字段完整覆盖：
python ~/.claude/skills/research/validate_json.py -f {project_dir}/fields.yaml -j {project_dir}/results/GitHub_Copilot.json
验证通过后才算完成任务。

Step 4: 等待与监控

等待当前批次完成
启动下一批
显示进度

Step 5: 汇总报告

全部完成后输出：

完成数量
失败/不确定标记的items
输出目录

Agent配置

后台执行: 是
Task Output: 禁用（agent完成时有明确输出文件）
断点续传: 是

Frequently asked questions

What does the Research Deep AI skill do?

读取调研outline，为每个item启动独立agent进行深度调研。禁用task output。

Why use Research Deep on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Weizhena/Deep-Research-skills/tree/master/skills/research-zh/research-deep. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Research Deep?

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 Deep?

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

Is the Research Deep AI skill free?

Yes. It is published on GitHub by Weizhena 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Research Deep - 深度调研

触发方式

执行流程

Step 1: 自动定位Outline

Step 2: 断点续传检查

Step 3: 分批执行

Step 4: 等待与监控

Step 5: 汇总报告

Agent配置

Frequently asked questions

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