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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

857

周安装

35

GitHub Stars

52

下载量

274
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/zenobi-us/dotfiles
仓库路径:skills/deep-research
安装命令:
npx skills add https://github.com/zenobi-us/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/zenobi-us/dotfiles --skill deep-research

简介

结构化深度研究框架,强调证据溯源与置信度评估。

  • 适用于学术写作、政策分析与复杂问题拆解场景。deep-research 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 输出包含思考过程、来源矩阵与矛盾点对比分析。
  • 强制要求每项主张附URL、访问日期与独立验证记录。
  • 禁止推测未证实信息,所有综合结论必须有直接依据支撑。

SKILL.md

⚠️ REQUEST VALIDATION (DO THIS FIRST)

CRITICAL: Before starting any research, validate that the request contains:

  1. Topic (required) - Clear description of what to research. Examples:

- "Compare authentication strategies in modern web frameworks" - "Investigate performance implications of different database indexing approaches" - "Research current best practices for handling TypeScript error types"

  1. Storage Path (optional) - Defer storage location rules to the miniproject skill. If a path is provided by the requester, treat it as a hint and still follow miniproject storage conventions.
  2. Things to Avoid (optional) - Topics, sources, or approaches to exclude from research. Examples:

- "Avoid paywalled academic papers" - "Skip marketing materials and focus on technical documentation" - "Exclude blog posts older than 2 years"

Rejection Protocol

If the request is missing topic, immediately reject with:

❌ Research request rejected. Missing required information:

Required:
- [ ] Topic: What should be researched?

Optional:
- [ ] Storage Path: Where should output files be written? (Handled by miniproject rules)
- [ ] Things to Avoid: Any topics or sources to exclude?

Example valid request:
"Research: React Server Components vs Client Components (with pros/cons analysis)
Storage: ./.memory/
Avoid: Paywalled papers, marketing content"

Do not proceed with research until the topic is provided.

Research Methodology

Phase 1: Topic Scoping & Planning

  • Decompose the research question into specific sub-questions
  • Identify primary, secondary, and tertiary source types
  • Plan a verification strategy before beginning searches
  • Define what constitutes "evidence" for this specific topic

Phase 2: Source Collection & Crawling

  • Use webfetch tool to gather content from authoritative sources
  • Search GitHub repositories for code examples, implementations, and discussions using gh_grep
  • Collect both primary sources (original research, official documentation) and secondary sources (analysis, reviews)
  • Document source URLs, publication dates, and credibility indicators
  • Aim for at least 3-5 independent, authoritative sources per key claim

Phase 3: Information Collation

  • Organize findings by theme/question
  • Note agreements and disagreements across sources
  • Identify patterns, outliers, and contradictions
  • Create a structured evidence map showing source-to-claim relationships

Phase 4: Verification & Fact-Checking

  • Cross-reference claims across multiple sources
  • Check publication dates and update status
  • Verify author credentials and source authority
  • Identify any sources with known biases or limitations
  • Mark confidence levels: high (3+ independent agreement), medium (2 sources), low (single source or conflicting)
  • Flag unverified claims clearly

Phase 5: Output Generation

When research is complete, write findings using the miniproject storage conventions and location rules.

Create a single output file per research unit with this required filename prefix:

research-{hash}-{parent_topic}-{child_topic}.md

Inside that file, use these sections (not separate files):

  • Thinking
  • Research
  • Verification
  • Insights
  • Summary

Output Guidelines

Thinking section

  • Record your research process and decisions
  • Note any rabbit holes explored or abandoned
  • Document assumptions and limitations
  • Explain how you approached verification
  • Include timestamps and progression of investigation

Research section

  • Organize by key themes or questions
  • Include direct quotes with source attribution
  • Note publication dates and source authority
  • Present both supporting and contradicting evidence
  • Use clear hierarchical structure

Verification section

  • Create a source credibility matrix
  • Document verification approach for each major claim
  • Show cross-reference patterns (which sources agree)
  • List confidence levels for each key finding
  • Identify gaps or unverifiable claims
  • Include URLs with access dates

Insights section

  • Synthesize patterns across sources
  • Identify implications and significance
  • Note emerging consensus vs. outlier views
  • Highlight surprising or counterintuitive findings
  • Suggest areas needing further research

Summary section

  • 1-2 paragraph executive summary
  • Key findings with confidence levels
  • Main limitations or caveats
  • Recommendations for using these findings
  • Suggested next steps for deeper investigation

Verification Evidence Standards

For each major claim, provide:

  1. Source URL - exact location of information
  2. Access Date - when you retrieved it
  3. Source Type - academic, official docs, news, community discussion, etc.
  4. Author/Publisher - who produced this content
  5. Confidence Level - based on independent source agreement
  6. Contradictions - any sources that disagree or qualify the claim

Critical Standards

  • No speculation: Flag anything not directly sourced
  • No synthesis without evidence: Don't combine sources into novel claims
  • No appeals to authority: Verify claims, not just who said them
  • Transparency: Show your work—readers must see your reasoning
  • Humility: Clearly state limitations and areas of uncertainty
  • Recency: Always note if information is outdated or superseded

Tools Available

  • webfetch: Retrieve and convert web content to markdown
  • gh_grep: Search GitHub for code patterns and examples across repositories
  • bash: Execute commands for data processing (use sparingly)
  • skill_use: Load expert skills if specialized knowledge needed
  • write: Output research findings
  • read: Review previously gathered information

When to Escalate

If you encounter:

  • Highly specialized technical topics beyond your scope, load relevant expert skills
  • Need for statistical analysis or data processing, use bash tools appropriately
  • Conflicting information that can't be resolved, document the disagreement thoroughly
  • Topics requiring real-time information (stock prices, weather, current events), note data freshness limitations

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

OpenCode

30.78%
按下载量换算84

Claude Code

21.82%
按下载量换算60

windsurf

17.12%
按下载量换算47

Cursor

13.18%
按下载量换算36

Codex

7.14%
按下载量换算20

Antigravity

3.3%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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