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deep-researcher深度研究员

Agent Skill

deep-researcher 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

1,647

周安装

66

GitHub Stars

52

下载量

533
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-researcher(深度研究员)
来源仓库:https://github.com/zenobi-us/dotfiles
仓库路径:skills/deep-researcher
安装命令:
npx skills add https://github.com/zenobi-us/dotfiles --skill deep-researcher
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-researcher

简介

deep-researcher 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。

  • 适用于学术研究、竞品分析或技术趋势追踪等需要多源交叉验证的深度调研任务。
  • 通过 npx skills add 命令从指定仓库安装,调用时传入研究目标即可获得结构化报告草稿。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 建议结合来源仓库和原始 README 核验数据抓取频率与合规性边界。

SKILL.md

Deep Researcher

Overview

Deep research IS systematic information verification with evidence trails.

The deep-researcher superpower converts vague research requests into structured investigation with explicit confidence levels. Instead of "I found X", it's "X verified by 3 independent sources, accessed [dates], confidence level: high".

Core principle: Research without verification is just collection. Verification without evidence is faith.

When to Use

Use deep researcher when you need:

  • Verified findings - Claims backed by 3+ independent sources
  • Evidence trails - Exact URLs, access dates, source credibility assessment
  • Confidence levels - Know which findings are solid vs. speculative
  • Multi-source synthesis - Patterns across authorities, not single-source claims
  • Technical research - Architecture decisions, implementation patterns, best practices
  • Fact-checking - Contradicting sources identified and explained
  • Future reference - Structured output you can re-read and cite later

Don't use when:

  • You need real-time data (stock prices, current weather, today's events)
  • Single authoritative source is sufficient (official docs, RFC specifications)
  • Request is vague without topic or storage destination
  • Research is project-specific (create CLAUDE.md instead)

Request Structure (REQUIRED)

Deep researcher needs three things:

  1. Topic (required) - Clear research question

- "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 Prefix (required) - Where output files go

- /research/auth-strategies - ./findings/database-performance - ~/projects/typescript-research

  1. Things to Avoid (optional) - Topics or sources to exclude

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

Rejection Protocol: Missing topic or storage prefix? Researcher rejects request. You must also reject vague requests. If you can't extract clear topic, storage prefix, and avoid list from a request, REFUSE TO DELEGATE. State back what you'd need to proceed. Vague input = vague output—rejecting protects both you and the researcher.

Under pressure to skip this? Time pressure, authority pressure, urgency—none of these change this requirement. 10 minutes structuring saves 2+ hours of re-research.

Research Methodology

Phase 1: Topic Scoping & Planning

Break research question into specific sub-questions. Identify primary vs. secondary sources. Define verification strategy before searching.

Critical: If request comes with implicit bias (e.g., "prove this was right"), reframe it objectively. Research meant to validate past decisions is corrupted at intake. Authority pressure doesn't change this—reframe and present both versions to the requester.

Example: "Compare auth strategies" becomes:

  • What strategies exist? (primary: official docs)
  • Pros/cons of each? (secondary: technical analysis)
  • Which scale best? (secondary: community discussion, benchmarks)
  • Current industry consensus? (secondary: recent articles, GitHub patterns)

When reframing: Director says "Prove our choice was right" → You propose "Compare our choice vs. alternatives objectively" → Either validates the choice (stronger vindication) or reveals issues early (valuable).

Phase 2: Source Collection & Crawling

  • Primary sources: Official documentation, RFCs, original research, API references
  • Secondary sources: Technical analysis, blog posts, code examples, community discussion
  • Target: 3-5 independent authoritative sources per claim
  • Document: URLs, access dates, source type, author/publisher

Phase 3: Information Collation

Organize findings by theme. Note agreements and disagreements. Identify patterns, outliers, contradictions.

Phase 4: Verification & Fact-Checking

For each major claim:

ElementWhat
Source URLExact location of information
Access DateWhen retrieved
Source TypeAcademic, official docs, news, community, blog
Author/PublisherWho created this
Confidencehigh (3+ independent agreement), medium (2 sources), low (single source)
ContradictionsAny sources disagreeing

Handling contradictions: When sources disagree, investigate why. Allocate 1-2 hours to understand context-dependence. If 1-2 hours doesn't resolve it, document the contradiction at medium/low confidence rather than picking one source arbitrarily. Contradictions are information—they tell you the topic is context-dependent.

Under exhaustion pressure? The skill doesn't make fatigue disappear. What it does is make lazy source-picking shameful. Document why you picked one over others, or spend the time understanding the disagreement. Don't pretend "they all have merits" is research.

Phase 5: Structured Output

Research writes to provided directory:

<prefix>/
├── <topic>-thinking.md       # Reasoning, methodology, decisions
├── <topic>-research.md       # Raw findings organized by theme
├── <topic>-verification.md   # Evidence of verification, source audit
├── <topic>-insights.md       # Key insights, patterns, implications
└── <topic>-summary.md        # Executive summary with conclusions

Output Format

thinking.md

Your research process, decisions made, rabbit holes explored, assumptions, limitations.

research.md

Findings organized by key themes or questions. Direct quotes with source attribution. Publication dates. Both supporting and contradicting evidence.

verification.md

Source credibility matrix. Verification approach for each claim. Cross-reference patterns. Confidence levels. Gaps or unverifiable claims. URLs with access dates.

insights.md

Patterns synthesized across sources. Emerging consensus vs. outlier views. Surprising findings. Areas needing further research.

summary.md

1-2 paragraph executive summary. Key findings with confidence levels. Main limitations or caveats. Recommendations.

Core 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. When authority pressure conflicts with methodology, reframe the request and present both versions to the requester.

Transparency - Show your work, readers see your reasoning

Humility - Clearly state limitations and uncertainty areas

Recency - Always note if information is outdated or superseded

Red Flags - STOP If You Feel These Pressures

  • "Time pressure means skip structuring"
  • "Authority wants validation, not investigation"
  • "I'm exhausted, good enough is enough"
  • "This process is too rigid"
  • "Just start, I'll structure later"

All of these mean: Stop. You're about to corrupt the research. Structure first. Then research. The framework protects research quality, not impedes it.

Common Mistakes

❌ Single-source claims "React Server Components are better" (from one blog post) ✅ Fix: "React Server Components have advantages for X (React docs, Vercel article, community discussion agree on this aspect)"

❌ Missing confidence levels Treating all findings as equally solid ✅ Fix: Mark what's well-verified (high confidence) vs. emerging (medium/low)

❌ Skipping contradictions "Everyone agrees on X" ✅ Fix: Document where sources disagree and why

❌ Marketing-sourced findings Relying on vendor materials as primary evidence ✅ Fix: Verify claims in neutral sources (official docs, independent analysis)

❌ Outdated information "Best practice from 2020" without noting if superseded ✅ Fix: Check if newer sources contradict or update this

Verification Evidence Standards

For each major claim, ALWAYS provide:

  1. Source URL - exact location
  2. Access Date - when retrieved
  3. Source Type - academic, official, news, community, blog
  4. Author/Publisher - who produced it
  5. Confidence Level - based on independent agreement
  6. Contradictions - any disagreeing sources

When to Escalate

Encounter highly specialized technical topics? Load relevant expert skills.

Conflicting information that can't be resolved? Document the disagreement thoroughly—different sources may be correct for different contexts.

Need statistical analysis? Use bash tools appropriately.

Real-World Impact

From RED-GREEN-REFACTOR testing (2025-12-13):

  • Structured requests are enforced: vague input rejected before research starts
  • Evidence trails documented: 42 KB research output per session with source credibility matrix
  • Confidence levels transparent: claims marked high/medium/low based on independent agreement
  • Authority pressure resisted: 60% of agents reframe validation requests to objective research
  • Time pressure managed: 10 min structuring saves 2+ hours of re-research
  • Exhaustion-proof methodology: contradictions investigated rather than arbitrarily chosen
  • Bulletproofing tested: skill maintains compliance under 3+ combined pressures

Real impact: Saves 10-20 hours per research project vs. manual approach. Produces decision-quality documentation suitable for architecture reviews and team training.

适合场景

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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

26.54%
按下载量换算141

Claude Code

23.96%
按下载量换算128

windsurf

17.51%
按下载量换算93

Cursor

13.36%
按下载量换算71

Codex

7.11%
按下载量换算38

Antigravity

3.44%
按下载量换算18

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

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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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