Szpitale-graph/README.md

Deep Research Skill for Claude Code

English | 中文

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English

Inspired by RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context

A structured research workflow skill for Claude Code, supporting two-phase research: outline generation (extensible) and deep investigation. Human-in-the-loop design ensures precise control at every stage.

Use Cases

• Academic Research: Paper surveys, benchmark reviews, literature analysis
• Technical Research: Technology comparison, framework evaluation, tool selection
• Market Research: Competitor analysis, industry trends, product comparison
• Due Diligence: Company research, investment analysis, risk assessment

Commands

Note

: Use run /research instead of /research directly, as slash commands conflict with built-in commands.

Command	Description
run /research	Generate research outline with items and fields
run /research/add-items	Add more items to existing outline
run /research/add-fields	Add more fields to existing outline
run /research/deep	Deep research each item with parallel agents
run /research/report	Generate markdown report from JSON results

Installation

Choose your language version:

# English version
cp -r skills/research-en ~/.claude/skills/research

# Chinese version
cp -r skills/research-zh ~/.claude/commands/research

# Required: Install agent
cp agents/web-search-agent.md ~/.claude/agents/

Workflow

Phase 1: Generate Outline

run /research <topic>

• Model knowledge generates initial items and field framework
• Web search supplements latest items
• User confirms and adjusts
• Outputs: outline.yaml (items + config) + fields.yaml (field definitions)

Phase 2: Deep Research

run /research/deep

• Parallel agents research each item (batch_size configurable)
• Each agent reads fields.yaml and outputs structured JSON
• Supports checkpoint resume
• Outputs: results/*.json

Optional: Expand Outline

run /research/add-items    # Add research targets via user input or web search
run /research/add-fields   # Add field definitions

Phase 3: Generate Report

run /research/report

• Generates Python script to convert JSON to markdown
• User selects summary fields for TOC
• Skips uncertain values automatically
• Outputs: report.md

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中文

灵感来源：RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context

Claude Code 的结构化调研工作流技能，支持两阶段调研：outline生成（可扩展）和深度调查。人在回路设计确保每个阶段的精确控制。

使用场景

• 学术研究：论文综述、benchmark评测、文献分析
• 技术研究：技术对比、框架评估、工具选型
• 市场研究：竞品分析、行业趋势、产品比较
• 尽职调查：公司研究、投资分析、风险评估

命令

注意：使用 run /research 而非直接 /research，因为斜杠命令与内置命令冲突。

命令	描述
run /research	生成包含items和fields的调研outline
run /research/add-items	向现有outline添加更多items
run /research/add-fields	向现有outline添加更多fields
run /research/deep	使用并行agents对每个item进行深度调研
run /research/report	从JSON结果生成markdown报告

安装

选择语言版本：

# 英文版
cp -r skills/research-en ~/.claude/skills/research

# 中文版
cp -r skills/research-zh ~/.claude/skills/research

# 必需：安装agent
cp agents/web-search-agent.md ~/.claude/agents/

工作流

阶段1：生成Outline

run /research <topic>

• 模型知识生成初始items和字段框架
• 网络搜索补充最新items
• 用户确认并调整
• 输出：outline.yaml（items + 配置）+ fields.yaml（字段定义）

阶段2：深度调研

run /research/deep

• 并行agents调研每个item（batch_size可配置）
• 每个agent读取fields.yaml并输出结构化JSON
• 支持断点续传
• 输出：results/*.json

可选：扩展Outline

run /research/add-items    # 通过用户输入或网络搜索添加调研对象
run /research/add-fields   # 添加字段定义

阶段3：生成报告

run /research/report

• 生成Python脚本将JSON转换为markdown
• 用户选择目录中显示的摘要字段
• 自动跳过不确定值
• 输出：report.md

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References / 参考文献

• RhinoInsight: Improving Deep Research through Control Mechanisms for Model Behavior and Context

License / 许可证

MIT