# genpark-dynamic-aspect-ratio-visual-patch-tokenizer-skill

> genpark-dynamic-aspect-ratio-visual-patch-tokenizer-skill — alphaparkinc-genpark-dynamic-aspect-ratio-visual-patch-tokenizer-skill. Use this tool when you need to dynamically tokenize visual patches with adjustable aspect ratios, solving problems in image and video processing, computer vision, and machine learning applications. It takes in visual data and outputs tokenized patches, enabling efficient processing and analysis. Ideal for use cases involving 2D representation learning, such as object detection and image classification.

Canonical page: https://skillsregistry.net/skills/alphaparkinc-genpark-dynamic-aspect-ratio-visual-patch-tokenizer-skill  
JSON: https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-dynamic-aspect-ratio-visual-patch-tokenizer-skill

## Description

Dynamic aspect ratio visual patch tokenizer & 2D RoPE (Qwen2.5-VL 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-dynamic-aspect-ratio-visual-patch-tokenizer-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-dynamic-aspect-ratio-visual-patch-tokenizer-skill"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-dynamic-aspect-ratio-visual-patch-tokenizer-skill` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/alphaparkinc-genpark-dynamic-aspect-ratio-visual-patch-tokenizer-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