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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。

科研2.3kskills/research-zh/research/SKILL.md

Install

Send this to Claude Code, Codex or Cursor. The agent checks the Skill for safety first and installs it only after you confirm.

读取 https://funcoding.ai/skills/weizhena/deep-research-skills/skills-research-zh-research/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

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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