# Hardware Probe

> Use this tool when you need to analyze hardware performance for optimal AI workload deployment, as it provides expert-system diagnostics and recommendations for GPU optimization and large language model inference. It takes system hardware specifications as input and outputs performance characteristics and optimization suggestions. Ideal for developers configuring AI deployments to ensure efficient hardware utilization.

Canonical page: https://skillsregistry.net/skills/yamaru-eu-hardware-probe  
JSON: https://api.skillsregistry.net/v1/skills/yamaru-eu-hardware-probe

## Description

Provides AI assistants with expert-system analysis of hardware performance characteristics, GPU diagnostics, and LLM optimization recommendations. Built with TypeScript and published on npm as @yamaru-eu/hardware-probe, it evaluates system hardware in the context of large language model inference requirements. Useful for developers configuring AI workload deployments.

## Trust

- **Trust score (0–1):** 0.64
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** devops-ci
- **Updated:** 2026-09-02

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/yamaru-eu-hardware-probe)
- **Repository:** <https://github.com/yamaru-eu/hardware-probe>

## 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": "yamaru-eu-hardware-probe"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/yamaru-eu-hardware-probe` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/yamaru-eu-hardware-probe/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