# VLArm

> VLArm — gconsigli-vlarm. Use this tool when you need to integrate vision, language, and action capabilities into a robotic system, such as controlling a robotic arm like HuggingFace's SO-100 model. VLArm solves problems in robotics and automation by enabling models to understand and respond to visual and linguistic inputs. It takes in code and model configurations as inputs and outputs a functional VLA model that can be deployed on a robotic arm.

Canonical page: https://skillsregistry.net/skills/gconsigli-vlarm  
JSON: https://api.skillsregistry.net/v1/skills/gconsigli-vlarm

## Description

Creating a VLA (Vision-Language-Action model) with HuggingFace's SO-100 robotic arm.

## Trust

- **Trust score (0–1):** 0.99
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/gconsigli/VLArm)

## 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": "gconsigli-vlarm"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/gconsigli-vlarm` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/gconsigli-vlarm/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