跨聊天、邮件与云文档统一检索,快速找回决策、资料和讨论记录
复制安装指令,让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "search" 技能: 1. 下载 https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/enterprise-search/skills/search/SKILL.md 2. 保存为 ~/.claude/skills/search/SKILL.md 3. 装好后重载技能,告诉我可以用了
帮我找一下上个月讨论过的新版定价方案文档,可能在邮件、聊天记录或云盘里。
返回最相关的文档、相关对话来源,以及可帮助定位的摘要信息。
我们之前决定是否要把移动端改版延期?请帮我找到对应的讨论和最终结论。
整理出相关讨论记录、决策结论,以及出现这些信息的具体位置。
帮我找一下关于客户A投诉升级的那次沟通,可能在项目系统、邮件或团队聊天里。
给出最可能的沟通记录、涉及人员和时间线,便于快速继续跟进。
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.
Before searching, determine which MCP sources are available. Attempt to identify connected tools from the available tool list. Common sources:
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.
Analyze the search query to understand:
from: — Filter by sender/authorin: — Filter by channel, folder, or locationafter: — Only results after this datebefore: — Only results before this datetype: — Filter by content type (message, email, doc, thread, file)For each available source, create a targeted sub-query using that source's native search syntax:
~~chat:
from: maps to sender, in: maps to channel/room, dates map to time range filters~~email:
from: maps to sender, dates map to time range filterstype: to attachment filters or subject-line searches as appropriate~~cloud storage:
~~project tracker:
~~CRM:
~~knowledge base:
Run all sub-queries simultaneously across available sources. Do not wait for one source before searching another.
For each source:
Deduplication:
Ranking factors:
…
运行 nf-core/Nextflow 流水线,完成 RNA-seq、变异检测与 ATAC-seq 数据分析
为特定组织定制 Claude Code 插件配置、连接器与工作流适配方案。
围绕客户问题进行多来源调研与溯源,快速整理背景并支持准确回复。
帮助你快速查询指标、分析趋势成因,并生成面向干系人的数据报告。
用于统计分析数据分布、趋势、异常与显著性检验,辅助得出可靠结论
帮助你用 Python 制作清晰专业的数据可视化并选择合适图表。
帮助用户检索过往 Claude Code 对话,快速找回事实、决策与上下文线索。
帮助用户检索 Markdown 知识库、笔记与文档,快速定位所需信息。
将复杂问题拆解为多源检索策略,并汇总高相关结果与备选路径。
基于多源网页检索与综合分析,生成带引用和来源标注的深度研究报告
在回答前检索过往对话,找回真实上下文并避免重复询问或误判新话题。
跨多个 Notion 信息源检索并整合,生成带引用的结构化文档与报告。