--- name: search description: Search across all connected sources in one query. Trigger with "find that doc about...", "what did we decide on...", "where was the conversation about...", or when looking for a decision, document, or discussion that could live in chat, email, cloud storage, or a project tracker. argument-hint: "" --- # Search Command > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../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 --- Attribution added by YourAIKit. It is not part of the upstream file. - Source: https://github.com/anthropics/knowledge-work-plugins/tree/main/enterprise-search/skills/search - Credit: Anthropic, Knowledge Work Plugins (enterprise-search) - Upstream copy: anthropics/knowledge-work-plugins at commit 33d3dea41a341b341d4f5da2e8801ae0c89be2b8, enterprise-search/skills/search/SKILL.md - Copyright: held by the upstream authors credited above - License: Apache-2.0. Full text: https://youraikit.com/licenses/anthropics-knowledge-work-plugins-enterprise-search/LICENSE - The full upstream license text (LICENSE) follows. License (LICENSE): ```text Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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