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search搜索

Agent Skill

search 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

35,640

周安装

1,461

GitHub Stars

11,688

下载量

12,480
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

2

许可证

unknown

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:search(搜索)
来源仓库:https://github.com/anthropics/knowledge-work-plugins
仓库路径:skills/search
安装命令:
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill search
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill search

简介

search 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词快速定位候选结果时使用。

  • 它可结合来源仓库、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或文件读写。
  • 适用于通用研究检索类任务。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装。

SKILL.md

Search Command

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Search across all connected MCP sources in a single query. Decompose the user's question, run parallel searches, and synthesize results.

Instructions

1. Check Available Sources

Before searching, determine which MCP sources are available. Attempt to identify connected tools from the available tool list. Common sources:

  • ~~chat — chat platform tools
  • ~~email — email tools
  • ~~cloud storage — cloud storage tools
  • ~~project tracker — project tracking tools
  • ~~CRM — CRM tools
  • ~~knowledge base — knowledge base tools

If no MCP sources are connected:

To search across your tools, you'll need to connect at least one source.
Check your MCP settings to add ~~chat, ~~email, ~~cloud storage, or other tools.

Supported sources: ~~chat, ~~email, ~~cloud storage, ~~project tracker, ~~CRM, ~~knowledge base,
and any other MCP-connected service.

2. Parse the User's Query

Analyze the search query to understand:

  • Intent: What is the user looking for? (a decision, a document, a person, a status update, a conversation)
  • Entities: People, projects, teams, tools mentioned
  • Time constraints: Recency signals ("this week", "last month", specific dates)
  • Source hints: References to specific tools ("in ~~chat", "that email", "the doc")
  • Filters: Extract explicit filters from the query:

- from: — Filter by sender/author - in: — Filter by channel, folder, or location - after: — Only results after this date - before: — Only results before this date - type: — Filter by content type (message, email, doc, thread, file)

3. Decompose into Sub-Queries

For each available source, create a targeted sub-query using that source's native search syntax:

~~chat:

  • Use available search and read tools for your chat platform
  • Translate filters: from: maps to sender, in: maps to channel/room, dates map to time range filters
  • Use natural language queries for semantic search when appropriate
  • Use keyword queries for exact matches

~~email:

  • Use available email search tools
  • Translate filters: from: maps to sender, dates map to time range filters
  • Map type: to attachment filters or subject-line searches as appropriate

~~cloud storage:

  • Use available file search tools
  • Translate to file query syntax: name contains, full text contains, modified date, file type
  • Consider both file names and content

~~project tracker:

  • Use available task search or typeahead tools
  • Map to task text search, assignee filters, date filters, project filters

~~CRM:

  • Use available CRM query tools
  • Search across Account, Contact, Opportunity, and other relevant objects

~~knowledge base:

  • Use semantic search for conceptual questions
  • Use keyword search for exact matches

4. Execute Searches in Parallel

Run all sub-queries simultaneously across available sources. Do not wait for one source before searching another.

For each source:

  • Execute the translated query
  • Capture results with metadata (timestamps, authors, links, source type)
  • Note any sources that fail or return errors — do not let one failure block others

5. Rank and Deduplicate Results

Deduplication:

  • Identify the same information appearing across sources (e.g., a decision discussed in ~~chat AND confirmed via email)
  • Group related results together rather than showing duplicates
  • Prefer the most authoritative or complete version

Ranking factors:

  • Relevance: How well does the result match the query intent?
  • Freshness: More recent results rank higher for status/decision queries
  • Authority: Official docs > wiki > chat messages for factual questions; conversations > docs for "what did we discuss" queries
  • Completeness: Results with more context rank higher

6. Present Unified Results

Format the response as a synthesized answer, not a raw list of results:

For factual/decision queries:

[Direct answer to the question]

Sources:
- [Source 1: brief description] (~~chat, #channel, date)
- [Source 2: brief description] (~~email, from person, date)
- [Source 3: brief description] (~~cloud storage, doc name, last modified)

For exploratory queries ("what do we know about X"):

[Synthesized summary combining information from all sources]

Found across:
- ~~chat: X relevant messages in Y channels
- ~~email: X relevant threads
- ~~cloud storage: X related documents
- [Other sources as applicable]

Key sources:
- [Most important source with link/reference]
- [Second most important source]

For "find" queries (looking for a specific thing):

[The thing they're looking for, with direct reference]

Also found:
- [Related items from other sources]

7. Handle Edge Cases

Ambiguous queries: If the query could mean multiple things, ask one clarifying question before searching:

"API redesign" could refer to a few things. Are you looking for:
1. The REST API v2 redesign (Project Aurora)
2. The internal SDK API changes
3. Something else?

No results:

I couldn't find anything matching "[query]" across [list of sources searched].

Try:
- Broader terms (e.g., "database" instead of "PostgreSQL migration")
- Different time range (currently searching [time range])
- Checking if the relevant source is connected (currently searching: [sources])

Partial results (some sources failed):

[Results from successful sources]

Note: I couldn't reach [failed source(s)] during this search.
Results above are from [successful sources] only.

Notes

  • Always search multiple sources in parallel — never sequentially
  • Synthesize results into answers, do not just list raw search results
  • Include source attribution so users can dig deeper
  • Respect the user's filter syntax and apply it appropriately per source
  • When a query mentions a specific person, search for their messages/docs/mentions across all sources
  • For time-sensitive queries, prioritize recency in ranking
  • If only one source is connected, still provide useful results from that source

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

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

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

Codex

38.25%
按下载量换算4,774

Claude

29.04%
按下载量换算3,624

Cursor

19.43%
按下载量换算2,425

Gemini CLI

9.62%
按下载量换算1,201

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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