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Written by AutoGineer from the skill’s own files, not by the author
The search skill acts as a central hub for information retrieval across an organization's connected tools. By decomposing user queries and executing parallel searches across chat platforms, email, cloud storage, project trackers, CRMs, and knowledge bases, it eliminates the need to manually check individual applications. The skill focuses on synthesizing disparate data points into a coherent, ranked response with clear source attribution, ensuring users receive relevant and timely information.
What it does
- Decomposes natural language queries into targeted sub-queries for specific platforms.
- Executes parallel searches across all connected MCP sources to maximize efficiency.
- Applies intelligent ranking based on relevance, freshness, and source authority.
- Deduplicates information found across multiple channels to provide a clean summary.
- Synthesizes raw search results into direct answers or structured summaries.
- Handles edge cases like ambiguous queries or partial source failures gracefully.
When to use it
- When you need to find a specific document, decision, or conversation across multiple tools.
- When you are unsure which platform contains the information you are looking for.
- When you need to aggregate status updates or project details from various sources.
- When you want a synthesized answer rather than a list of raw search results.
Advantages
- Saves time by searching all connected tools in a single query.
- Reduces context switching between different applications.
- Provides context-aware answers rather than just links or file names.
- Maintains transparency by citing the specific sources used for the answer.
Usage examples
- “Find that doc about the Q4 marketing strategy.”
- The agent searches cloud storage and project trackers, returning the document link along with a summary of recent related discussions found in chat.
- “What did we decide on the API redesign?”
- The agent synthesizes the final decision from project tracker tickets and confirms the context via relevant email threads and chat logs.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/anthropics/search-5tls/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip search.zip -d .agents/skills
Documentation by Anthropic
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/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)
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 filtersUse 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 filtersMap
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
Files
- ATTRIBUTION.mdAdded by AutoGineer944 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.4 KiB
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Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Anthropic
- Publisher
- Anthropic (Official)
- Source
- anthropics/knowledge-work-plugins · enterprise-search/skills/search · commit e050c2e
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from enterprise-search/LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Anthropic or Anthropic.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download search.zip