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deep-research深入研究

Agent Skill

deep-research 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

465

周安装

19

GitHub Stars

2

下载量

150
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:deep-research(深入研究)
来源仓库:https://github.com/mhagrelius/dotfiles
仓库路径:skills/deep-research
安装命令:
npx skills add https://github.com/mhagrelius/dotfiles --skill deep-research
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/mhagrelius/dotfiles --skill deep-research

简介

deep-research 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。

  • 适用于深度研究、数据分析和信息整合等研究检索类任务。
  • 通过关键词、任务场景或来源线索进行快速定位和结果筛选。
  • 安装命令:npx skills add https://github.com/mhagrelius/dotfiles --skill deep-research
  • 建议确认权限范围和维护状态,注意是否会触发联网或文件读写操作。

SKILL.md

Deep Research

Autonomous multi-agent research system. Dispatches parallel sub-agents, stores findings to files, synthesizes into briefs or reports.

Core principle: Planning → Parallel research agents → File-based findings → Synthesis = high quality research with minimal context usage.

When to Use

digraph when_to_use {
    "User requests research?" [shape=diamond];
    "Quick factual lookup?" [shape=diamond];
    "Use single search tool directly" [shape=box];
    "Multiple sources or synthesis needed?" [shape=diamond];
    "deep-research" [shape=box];

    "User requests research?" -> "Quick factual lookup?" [label="yes"];
    "Quick factual lookup?" -> "Use single search tool directly" [label="yes"];
    "Quick factual lookup?" -> "Multiple sources or synthesis needed?" [label="no"];
    "Multiple sources or synthesis needed?" -> "deep-research" [label="yes"];
}

Use for: Technical research, domain knowledge, market analysis, architectural patterns, comparing approaches, learning complex topics

Don't use for: Single fact lookups, specific URL fetches, questions answerable in one search

The Process

digraph process {
    rankdir=TB;

    "Create research directory in scratchpad" -> "Dispatch Query Analyzer agent";
    "Dispatch Query Analyzer agent" -> "Analyzer writes research-plan.md";
    "Analyzer writes research-plan.md" -> "Read plan, dispatch N Research agents IN PARALLEL";
    "Read plan, dispatch N Research agents IN PARALLEL" -> "Each agent writes findings-{thread}.md";
    "Each agent writes findings-{thread}.md" -> "Wait for all agents";
    "Wait for all agents" -> "Dispatch Synthesizer agent";
    "Dispatch Synthesizer agent" -> "Synthesizer reads all findings, writes final-output.md";
    "Synthesizer reads all findings, writes final-output.md" -> "Read final output, present summary to user";
}

Quick Reference

Phase 1: Planning (Query Analyzer Agent)

Uses ./query-analyzer-prompt.md. Writes research-plan.md containing:

  • Query type: technical | domain | hybrid
  • Complexity: simple (2-3 agents) | moderate (3-4) | complex (5-6)
  • Research threads with source recommendations
  • Output format recommendation: brief | report

Phase 2: Parallel Research

Uses ./research-agent-prompt.md. Each agent:

  1. Invokes exa-search skill for source strategy
  2. Executes searches (Exa-primary, see Source Selection below)
  3. Writes findings-{thread-name}.md

Source Selection:

Query SignalPrimary Source
Code, APIs, librariesmcp__exa__get_code_context_exa
Concepts, analysis, opinionsmcp__exa__web_search_exa
Video explanations neededyt-transcribe skill
Very recent news (< 1 week)WebSearch fallback

Phase 3: Synthesis

Uses ./synthesizer-prompt.md. Reads all findings files, writes final-output.md:

  • Actionable Brief (~300 words): Simple query + clear consensus
  • Structured Report (~1500 words): Complex query or conflicting findings

Agent Dispatch Methods

For complex queries (4+ threads): Use Task tool with subagent_type: "general-purpose" for true sub-agent isolation. Dispatch all research agents in a single message (parallel Task calls).

For simpler queries (2-3 threads): Parallel tool calls within same context is acceptable - make all searches simultaneously, then write findings files.

Either way: research threads must execute in parallel, not sequentially.

File Structure

{scratchpad}/deep-research-{timestamp}/
├── research-plan.md
├── findings-*.md
└── final-output.md

Common Mistakes

MistakeFix
Doing research yourself instead of dispatching agentsAlways use the three-phase architecture
Keeping findings in context instead of filesEach agent MUST write to files
Sequential research agentsDispatch all research agents in PARALLEL
Skipping planning phaseAlways run Query Analyzer first
Using WebSearch as defaultExa is primary; WebSearch only for very recent news

Red Flags - STOP

  • "I'll just do a quick search myself" → Use the full process
  • "I don't need to write files for this" → Files are mandatory
  • "I'll research these topics one at a time" → Parallel dispatch
  • "This is simple, I'll skip planning" → Always plan first

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

28.8%
按下载量换算43

Gemini CLI

22.42%
按下载量换算34

Antigravity

17.65%
按下载量换算26

windsurf

14.32%
按下载量换算21

trae

7.65%
按下载量换算11

OpenCode

3.81%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。

来源信息

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