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deep-research深入研究

Agent Skill

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

总安装

2,188

周安装

94

GitHub Stars

21

下载量

767
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/thepexcel/agent-skills
仓库路径:skills/deep-research
安装命令:
npx skills add https://github.com/thepexcel/agent-skills --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/thepexcel/agent-skills --skill deep-research

简介

deep-research 用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备,适合清洗字段、汇总数据和生成统计口径。

  • 适用于数据分析和处理任务,可发现异常并生成可读说明。
  • 通过 npx skills add 命令从 GitHub 仓库安装,支持主流 AI 宿主环境。
  • 使用时需确认数据来源和时间范围,涉及敏感数据时应先确认脱敏边界。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Deep Research

Better than plain websearch. Faster than full research pipelines.

What makes it better:

  1. Landscape Scan — discovers what exists before searching specifics (avoids blind spots)
  2. Recency Pulse — catches releases from last 7-30 days, searches upstream providers
  3. Parallel search — 2-3 queries at once, multiple angles simultaneously

For rigorous research (hypotheses, COMPASS audit, Red Team, full report) → /deep-research-pro


Step 1: CLASSIFY

TypeWhenDo
ASingle factSearch → answer directly
BMulti-fact / comparisonSCAN → RECENCY → SEARCH → Synthesize
CJudgment / recommendationB + flag uncertainty + note limitations

Tiers: Quick (5-10 sources) · Standard (10-20 sources)


Step 2: LANDSCAPE SCAN *(skip for Type A)*

Map what exists before searching specifics. Never use known names in scan queries — you'll miss things that exist but you don't know about yet.

❌ "DeepSeek Qwen performance 2026"   ← only finds what you already know
✅ "China open source LLM list 2026"  ← discovers the full landscape

Queries (parallel):

WebSearch: "[topic] landscape overview [current year]"
WebSearch: "top [topic] list [current year]"
WebSearch: "[topic] all options [current year]"

Extract entity names → split into Discovered (new) vs Confirmed (updated).


Step 3: RECENCY PULSE *(mandatory for tech/AI topics)*

Yearly searches miss releases from last week. Downstream product news lags upstream by weeks.

Map supply chain first: Who makes the underlying tech? → Search them directly.

WebSearch: "[topic] latest news [current month] [current year]"
WebSearch: "[upstream provider] latest release [current month] [current year]"

Example — researching "Microsoft Copilot": Upstream = OpenAI + Anthropic → search both directly, don't rely on Microsoft announcements alone.

Flag anything from last 7-30 days as RECENT or BREAKING.


Step 4: SEARCH

Run queries in parallel (single message, multiple tool calls):

WebSearch: "[topic] [current year]"
WebSearch: "[topic] limitations problems"
WebSearch: "[topic] vs alternatives comparison"

Stop when: 3 consecutive searches add <10% new info (saturation) or sources converge on same answer.

URL fallback (403/blocked):

curl -s --max-time 60 "https://r.jina.ai/https://example.com"

Claim confidence:

  • C1 (key claims) — need 2+ sources + confidence note: HIGH / MEDIUM / LOW
  • C2 (supporting) — citation required
  • C3 (common knowledge) — cite only if contested

Never state C1 without citing [N]. If no source found → say so.


Step 5: SYNTHESIZE + SAVE

For each key finding, answer:

  • แล้วยังไง (So what)? — why does this matter?
  • ต้องทำอะไร (Now what)? — action to take?

If sources conflict: flag explicitly — "Source A says X, Source B says Y — likely because [reason]."

Always save output:

research/[topic-slug]-[YYYY-MM-DD].md

When to Ask vs Just Do

AskJust do
Topic too broad → "อยากเน้นมุมไหนคะ?"Choose search queries
Interesting sub-topic foundFormat output
Sources conflict on key pointType A questions

Related Skills

  • /deep-research-pro — Full pipeline: hypotheses, QUEST queries, COMPASS audit, Red Team, formal report
  • /boost-intel — Stress-test a research finding before making a decision
  • /generate-creative-ideas — Cross-industry creative research (no web search needed)

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

28.39%
按下载量换算218

OpenCode

24.59%
按下载量换算189

windsurf

15.22%
按下载量换算117

Codex

12.3%
按下载量换算94

Antigravity

7.75%
按下载量换算59

Gemini CLI

3.32%
按下载量换算25

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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