search — Claude Code skill: what it does, how to install
Runs parallel queries across connected MCP tools and synthesizes the findings into a single answer. Helpful when decisions or notes are scattered across chat, email, and storage.
npx skills add anthropics/knowledge-work-plugins --skill search -a claude-codeWhat search does
The skill takes a search phrase, breaks it into source-specific sub-queries, and runs them across all connected MCP integrations at once. It can query chat platforms, email, cloud storage, issue trackers, CRMs, and knowledge bases using native syntax and filters like from:, in:, after:, before:, and type:.
It groups duplicate findings from different sources and ranks them by relevance, freshness, and authority. Official documents take precedence for factual questions, while message threads take priority for discussions. A single failing connector will not abort searches across the remaining services.
Without at least one connected MCP source, the skill cannot return anything useful. When a request is ambiguous, it pauses to ask a single clarifying question instead of guessing.
What it helps with
- locate a past architecture decision scattered across emails and chat rooms
- find mentions of a teammate across all connected project tools
- track down a document in cloud storage using context from a conversation
- group related records from an issue tracker and a knowledge base
Example prompts for the agent
- find that doc about the database migration
- what did we decide on the API redesign from:alex
- where was the conversation about customer onboarding
How to install search
1Skills CLI (Vercel)
npx skills add anthropics/knowledge-work-plugins --skill search -a claude-code2Manually: put the files in place
~/.claude/skills/search/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/search/
1Skills CLI (Vercel)
npx skills add anthropics/knowledge-work-plugins --skill search -a codex2Manually: put the files in place
.agents/skills/search/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add anthropics/knowledge-work-plugins --skill search -a cursor2Manually: put the files in place
.cursor/skills/search/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install anthropics/knowledge-work-plugins search2Manually: put the files in place
.github/skills/search/SKILL.md1Skills CLI (Vercel)
npx skills add anthropics/knowledge-work-plugins --skill search -a gemini-cli2Platform CLI
gemini skills install https://github.com/anthropics/knowledge-work-plugins --path enterprise-search/skills/searchManually: put the files in place
.devin/skills/search/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add anthropics/knowledge-work-plugins --skill search -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set89
The whole set →Similar
Questions about search
How to install search in Claude Code?
Run npx skills add anthropics/knowledge-work-plugins --skill search -a claude-code. It installs the skill configuration to ~/.claude/skills/search/SKILL.md.
What does the search skill do?
It breaks down your question into parallel sub-queries for all active MCP sources, runs them simultaneously, and synthesizes the results with source citations.
What happens if no MCP sources are connected?
The skill notifies you that at least one MCP tool is required. You will need to configure connectors such as chat, email, cloud storage, or an issue tracker first.
Source text
SKILL.md · Apache-2.0
---
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: "<query>"
---
# 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
© anthropics · Apache-2.0 · anthropics/knowledge-work-plugins