Research Skill for Cursor

by Weizhena

对目标话题进行初步调研，生成调研outline。用于学术调研、benchmark调研、技术选型等场景。

3k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

1853

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/Weizhena/Deep-Research-skills --skill research

What comes with it

6 643 bytes besides the instruction

validate_json.py

What it tells the agent to use

found in the instruction text

WebSearch reads your files

The instruction itself

6 sections, as written by the author

Research Skill - 初步调研

触发方式

/research <topic>

执行流程

Step 1: 模型内部知识生成初步框架

基于topic，利用模型已有知识生成：

该领域的主要研究对象/items列表
建议的调研字段框架

输出{step1_output}，使用AskUserQuestion确认：

items列表是否需要增减？
字段框架是否满足需求？

Step 2: Web Search补充

使用AskUserQuestion询问时间范围（如：最近6个月、2024年至今、不限）。

参数获取：

{topic}: 用户输入的调研话题
{YYYY-MM-DD}: 当前日期
{step1_output}: Step 1生成的完整输出内容
{time_range}: 用户指定的时间范围

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。

启动1个web-search-agent（后台），Prompt模板：

prompt = f"""## 任务
调研话题: {topic}
当前日期: {YYYY-MM-DD}

基于以下初步框架，补充最新items和推荐调研字段。

## 已有框架
{step1_output}

## 目标
1. 验证已有items是否遗漏重要对象
2. 根据遗漏对象进行补充items
3. 继续搜索{topic}相关且{time_range}内的items并补充
4. 补充新fields

## 输出要求
直接返回结构化结果（不写文件）：

### 补充Items
- item_name: 简要说明（为什么应该加入）
...

### 推荐补充字段
- field_name: 字段描述（为什么需要这个维度）
...

### 信息来源
- [来源1](url1)
- [来源2](url2)
"""

One-shot示例（假设调研AI Coding发展史）：

## 任务
调研话题: AI Coding 发展史
当前日期: 2025-12-30

基于以下初步框架，补充最新items和推荐调研字段。

## 已有框架
### Items列表
1. GitHub Copilot: Microsoft/GitHub开发，首个主流AI编程助手
2. Cursor: AI-first IDE，基于VSCode
...

### 字段框架
- 基本信息: name, release_date, company
- 技术特性: underlying_model, context_window
...

## 目标
1. 验证已有items是否遗漏重要对象
2. 根据遗漏对象进行补充items
3. 继续搜索AI Coding 发展史相关且2024年至今内的items并补充
4. 补充新fields

## 输出要求
直接返回结构化结果（不写文件）：

### 补充Items
- item_name: 简要说明（为什么应该加入）
...

### 推荐补充字段
- field_name: 字段描述（为什么需要这个维度）
...

### 信息来源
- [来源1](url1)
- [来源2](url2)

Step 3: 询问用户已有字段

使用AskUserQuestion询问用户是否有已定义的字段文件，如有则读取并合并。

Step 4: 生成Outline（分离文件）

合并{step1_output}、{step2_output}和用户已有字段，生成两个文件：

outline.yaml（items + 配置）：

topic: 调研主题
items: 调研对象列表
execution:
batch_size: 并行agent数量（需AskUserQuestion确认）
items_per_agent: 每个agent调研项目数（需AskUserQuestion确认）
output_dir: 结果输出目录（默认./results）

fields.yaml（字段定义）：

字段分类和定义
每个字段的name、description、detail_level
detail_level分层：极简 → 简要 → 详细
uncertain: 不确定字段列表（保留字段，deep阶段自动填充）

Step 5: 输出并确认

创建目录: ./{topic_slug}/
保存: outline.yaml 和 fields.yaml
展示给用户确认

输出路径

{当前工作目录}/{topic_slug}/
  ├── outline.yaml    # items列表 + execution配置
  └── fields.yaml     # 字段定义

后续命令

/research-add-items - 补充items
/research-add-fields - 补充字段
/research-deep - 开始深度调研

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How to use it

Copy the folder

Take weizhena/deep-research-zh-research from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.