Embedding Search
Find relevant documents or data points based on semantic similarity using vector embeddings powered by winsorllc.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add embedding-search npx -- -y @trustedskills/embedding-search
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"embedding-search": {
"command": "npx",
"args": [
"-y",
"@trustedskills/embedding-search"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill enables AI agents to perform semantic searches using embeddings. It allows for finding relevant information based on meaning rather than keyword matches, improving search accuracy and recall. The agent can retrieve documents or data points that are conceptually similar to a given query, even if they don't share the same words.
When to use it
- Knowledge Base Search: Quickly find relevant articles or documentation within a large internal knowledge base.
- Product Recommendation: Suggest products based on user descriptions of desired features or benefits.
- Content Discovery: Help users discover related content based on an article they are currently reading.
- Question Answering: Retrieve answers to complex questions that require understanding the context and meaning behind the query.
Key capabilities
- Semantic search using embeddings
- Retrieval of conceptually similar information
- Improved search accuracy compared to keyword-based methods
Example prompts
- "Find documents related to 'customer churn prevention strategies'."
- "What products are similar to a lightweight, waterproof hiking backpack?"
- "I'm looking for articles about improving team collaboration."
Tips & gotchas
The effectiveness of this skill depends on the quality and relevance of the underlying embeddings. Ensure your data is properly vectorized and indexed for optimal search results.
Tags
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Security Audits
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