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Deep Research Skill for Claude Code

English | 中文

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

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/

Commands

Note

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

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

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

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/

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。

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

References / 参考文献

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

License / 许可证

MIT

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