bighardperson/computer-science-skills-collection/deep-research

Claude Code and agent skill by bighardperson/computer-science-skills-collection

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Structured deep research workflow with human-in-the-loop control. Use /research to generate research outline, /research-deep for parallel web search across items, /research-report to compile markdown reports. Supports academic research, benchmark research, technology selection, market analysis, and due diligence. Triggers: 'deep research', 'research topic', 'benchmark comparison', 'technology survey', 'market analysis'. Requires: WebSearch capability.

Install

npx vskill@latest install bighardperson/computer-science-skills-collection/deep-research

SKILL.md

Deep Research — 结构化深度调研工作流

基于 Deep-Research-skills 项目,提供人机协作的结构化调研能力。

核心命令

命令说明
/research <topic>初步调研,生成 outline.yaml + fields.yaml
/research-deep并行深度搜索,逐项收集数据
/research-report汇总生成 Markdown 报告
/research-add-items追加调研条目
/research-add-fields追加调研字段

工作流程

/research "AI Agent 2025"
↓ 生成大纲(items + fields)
↓ 用户确认/修改
/research-deep
↓ 并行 web-search-agent 逐项搜索
↓ 结果写入 results/
/research-report
↓ 汇总为 report.md

适用场景

  • 学术调研:论文综述、Benchmark 对比
  • 技术选型:框架评估、工具对比
  • 市场调研:竞品分析、行业趋势
  • 尽职调查:公司研究、投资分析

目录结构

deep-research/
├── research/SKILL.md # /research 命令(中文版)
├── research-deep/SKILL.md # /research-deep 命令
├── research-report/SKILL.md # /research-report 命令
├── research-add-items/SKILL.md
├── research-add-fields/SKILL.md
├── research-en/ # 英文版本
│ ├── research/SKILL.md
│ ├── research-deep/SKILL.md
│ ├── research-report/SKILL.md
│ ├── research-add-items/SKILL.md
│ └── research-add-fields/SKILL.md
└── agents/ # 搜索代理
├── web-search-agent.md
└── web-search-modules/ # 搜索策略模块

输出示例

{topic_slug}/
├── outline.yaml # 调研条目 + 执行配置
├── fields.yaml # 字段定义
├── results/ # 逐项搜索结果
│ ├── item_1.yaml
│ ├── item_2.yaml
│ └── ...
└── report.md # 最终报告

致谢

  • 作者:Weizhena
  • 灵感:RhinoInsight — Improving Deep Research through Control Mechanisms for Model Behavior and Context

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
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Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
56
Repository forks
10
7-day trend score
20

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