research

# Research Skill - Preliminary Research

## Trigger
`/research <topic>`

## Workflow

### Step 1: Generate Initial Framework from Model Knowledge
Based on topic, use model's existing knowledge to generate:
- Main research objects/items list in this domain
- Suggested research field framework

Output {step1_output}, use AskUserQuestion to confirm:
- Need to add/remove items?
- Does field framework meet requirements?

### Step 2: Web Search Supplement
Use AskUserQuestion to ask for time range (e.g., last 6 months, since 2024, unlimited).

**Parameter Retrieval**:
- `{topic}`: User input research topic
- `{YYYY-MM-DD}`: Current date
- `{step1_output}`: Complete output from Step 1
- `{time_range}`: User specified time range

**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.

Launch 1 web-search-agent (background), **Prompt Template**:
```python
prompt = f"""## Task
Research topic: {topic}
Current date: {YYYY-MM-DD}

Based on the following initial framework, supplement latest items and recommended research fields.

## Existing Framework
{step1_output}

## Goals
1. Verify if existing items are missing important objects
2. Supplement items based on missing objects
3. Continue searching for {topic} related items within {time_range} and supplement
4. Supplement new fields

## Output Requirements
Return structured results directly (do not write files):

### Supplementary Items
- item_name: Brief explanation (why it should be added)
...

### Recommended Supplementary Fields
- field_name: Field description (why this dimension is needed)
...

### Sources
- [Source1](url1)
- [Source2](url2)
"""
```

**One-shot Example** (assuming researching AI Coding History):
```
## Task
Research topic: AI Coding History
Current date: 2025-12-30

Based on the following initial framework, supplement latest items and recommended research fields.

## Existing Framework
### Items List
1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant
2. Cursor: AI-first IDE, based on VSCode
...

### Field Framework
- Basic Info: name, release_date, company
- Technical Features: underlying_model, context_window
...

## Goals
1. Verify if existing items are missing important objects
2. Supplement items based on missing objects
3. Continue searching for AI Coding History related items within since 2024 and supplement
4. Supplement new fields

## Output Requirements
Return structured results directly (do not write files):

### Supplementary Items
- item_name: Brief explanation (why it should be added)
...

### Recommended Supplementary Fields
- field_name: Field description (why this dimension is needed)
...

### Sources
- [Source1](url1)
- [Source2](url2)
```

### Step 3: Ask User for Existing Fields
Use AskUserQuestion to ask if user has existing field definition file, if so read and merge.

### Step 4: Generate Outline (Separate Files)
Merge {step1_output}, {step2_output} and user's existing fields, generate two files:

**outline.yaml** (items + config):
- topic: Research topic
- items: Research objects list
- execution:
  - batch_size: Number of parallel agents (confirm with AskUserQuestion)
  - items_per_agent: Items per agent (confirm with AskUserQuestion)
  - output_dir: Results output directory (default: ./results)

**fields.yaml** (field definitions):
- Field categories and definitions
- Each field's name, description, detail_level
- detail_level hierarchy: brief -> moderate -> detailed
- uncertain: Uncertain fields list (reserved field, auto-filled in deep phase)

### Step 5: Output and Confirm
- Create directory: `./{topic_slug}/`
- Save: `outline.yaml` and `fields.yaml`
- Show to user for confirmation

## Output Path
```
{current_working_directory}/{topic_slug}/
  ├── outline.yaml    # items list + execution config
  └── fields.yaml     # field definitions
```

## Follow-up Commands
- `/research-add-items` - Supplement items
- `/research-add-fields` - Supplement fields
- `/research-deep` - Start deep research

## 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**：
```python
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
### 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

## 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。

## 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