Research Deep

Research Deep - Deep Research

Trigger

/research-deep

Workflow

Step 1: Auto-locate Outline

Find */outline.yaml file in current working directory, read items list, execution config (including items_per_agent).

Step 2: Resume Check

• Check completed JSON files in output_dir
• Skip completed items

Step 3: Batch Execution

• Batch by batch_size (need user approval before next batch)
• Each agent handles items_per_agent items
• Launch web-search-agent (background parallel, disable task output)

Parameter Retrieval:

• {topic}: topic field from outline.yaml
• {item_name}: item's name field
• {item_related_info}: item's complete yaml content (name + category + description etc.)
• {output_dir}: execution.output_dir from outline.yaml (default: ./results)
• {fields_path}: absolute path to {topic}/fields.yaml
• {output_path}: absolute path to {output_dir}/{item_name_slug}.json (slugify item_name: replace spaces with _, remove special chars)

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

Prompt Template:

prompt = f"""## Task
Research {item_related_info}, output structured JSON to {output_path}

## Field Definitions
Read {fields_path} to get all field definitions

## Output Requirements
1. Output JSON according to fields defined in fields.yaml
2. Mark uncertain field values with [uncertain]
3. Add uncertain array at the end of JSON, listing all uncertain field names
4. All field values must be in English

## Output Path
{output_path}

## Validation
After completing JSON output, run validation script to ensure complete field coverage:
python ~/.claude/skills/research/validate_json.py -f {fields_path} -j {output_path}
Task is complete only after validation passes.
"""

One-shot Example (assuming researching GitHub Copilot):

## Task
Research name: GitHub Copilot
category: International Product
description: Developed by Microsoft/GitHub, first mainstream AI coding assistant, ~40% market share, output structured JSON to /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json

## Field Definitions
Read /home/weizhena/AIcoding/aicoding-history/fields.yaml to get all field definitions

## Output Requirements
1. Output JSON according to fields defined in fields.yaml
2. Mark uncertain field values with [uncertain]
3. Add uncertain array at the end of JSON, listing all uncertain field names
4. All field values must be in English

## Output Path
/home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json

## Validation
After completing JSON output, run validation script to ensure complete field coverage:
python ~/.claude/skills/research/validate_json.py -f /home/weizhena/AIcoding/aicoding-history/fields.yaml -j /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
Task is complete only after validation passes.

Step 4: Wait and Monitor

• Wait for current batch to complete
• Launch next batch
• Display progress

Step 5: Summary Report

After all complete, output:

• Completion count
• Failed/uncertain marked items
• Output directory

Agent Config

• Background execution: Yes
• Task Output: Disabled (agent has explicit output file when complete)
• Resume support: Yes

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：

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

## 字段定义
读取 /home/weizhena/AIcoding/aicoding-history/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
/home/weizhena/AIcoding/aicoding-history/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 /home/weizhena/AIcoding/aicoding-history/fields.yaml -j /home/weizhena/AIcoding/aicoding-history/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

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 /home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json

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