# genpark-flash-attention-dense-passage-ranker-skill

> genpark-flash-attention-dense-passage-ranker-skill — alphaparkinc-genpark-flash-attention-dense-passage-ranker-skill. Use this tool when you need to efficiently rank passages or extract sequence embeddings from text data, solving problems such as information retrieval, question answering, and text summarization. It takes in text passages and queries as input and outputs ranked passages or dense vector embeddings. Ideal for use cases where fast and accurate text ranking or embedding extraction is required, such as search engines, chatbots, or language models.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-flash-attention-dense-passage-ranker-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-flash-attention-dense-passage-ranker-skill

## Description

Flash-attention dense passage ranker & sequence embedding extractor (Hugging Face TEI)

## 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-flash-attention-dense-passage-ranker-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-flash-attention-dense-passage-ranker-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-flash-attention-dense-passage-ranker-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-flash-attention-dense-passage-ranker-skill/pull`

---
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