# genpark-vector-embeddings-similarity-search-engine-skill

> genpark-vector-embeddings-similarity-search-engine-skill — alphaparkinc-genpark-vector-embeddings-similarity-search-engine-skill. Use this tool when you need to efficiently search and retrieve similar items from a large dataset based on vector embeddings. It solves problems related to semantic search, recommendation systems, and data clustering by utilizing cosine similarity to measure the similarity between vectors. The engine takes in vector embeddings as input and outputs a list of similar items, making it ideal for applications requiring fast and accurate similarity searches.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-vector-embeddings-similarity-search-engine-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-vector-embeddings-similarity-search-engine-skill

## Description

Vector embeddings cosine similarity search engine

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/alphaparkinc/genpark-vector-embeddings-similarity-search-engine-skill)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "alphaparkinc-genpark-vector-embeddings-similarity-search-engine-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-vector-embeddings-similarity-search-engine-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-vector-embeddings-similarity-search-engine-skill/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt