Vector Search
Quickly retrieve relevant documents from large datasets using semantic similarity search powered by vector embeddings.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add vector-search npx -- -y @trustedskills/vector-search
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"vector-search": {
"command": "npx",
"args": [
"-y",
"@trustedskills/vector-search"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The vector-search skill enables AI agents to perform similarity searches within a dataset represented as vectors. This allows retrieval of items most closely related to a given query, based on semantic meaning rather than keyword matching. It's useful when you need to find similar documents, products, or other data points.
When to use it
- Product Recommendations: Find products similar to one a user is viewing.
- Document Retrieval: Retrieve relevant documents from a large knowledge base based on the meaning of a query.
- Semantic Search: Allow users to search for information using natural language, rather than specific keywords.
- Content Discovery: Suggest related articles or content pieces based on what a user is currently consuming.
Key capabilities
- Vector similarity search
- Dataset indexing
- Query processing
Example prompts
- "Find documents similar to 'AI ethics and bias'."
- "What products are most like this one?" (referring to an item)
- “Search for articles related to sustainable energy.”
Tips & gotchas
The skill requires a pre-existing dataset that has been converted into vector embeddings. Ensure your data is properly vectorized before using the vector-search skill for optimal results.
Tags
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