# genpark-embedded-vector-hnsw-nearest-neighbor-index-skill

> genpark-embedded-vector-hnsw-nearest-neighbor-index-skill — alphaparkinc-genpark-embedded-vector-hnsw-nearest-neighbor-index-skill. Use this tool when you need to efficiently search for similar embedded vectors in high-dimensional spaces, solving problems like duplicate detection, recommendation systems, and semantic search. It takes in embedded vectors as input and outputs the nearest neighbors, allowing for fast and accurate querying. Ideal for applications requiring scalable and performant similarity searches, such as natural language processing and computer vision tasks.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-embedded-vector-hnsw-nearest-neighbor-index-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-embedded-vector-hnsw-nearest-neighbor-index-skill

## Description

Embedded vector HNSW approximate nearest neighbor index (Chroma style)

## 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-embedded-vector-hnsw-nearest-neighbor-index-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-embedded-vector-hnsw-nearest-neighbor-index-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-embedded-vector-hnsw-nearest-neighbor-index-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-embedded-vector-hnsw-nearest-neighbor-index-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