# RaceSimQA

> RaceSimQA — alxliv-racesimqa. Use this tool when you need to perform QA regression testing on racing car simulations, identifying issues and optimizing performance through analytics and data visualization. It solves problems related to simulation accuracy and reliability, providing outputs such as AI-generated summaries and LLM chat support. Input your simulation data to receive actionable insights and improve overall simulation quality.

Canonical page: https://skillsregistry.net/skills/alxliv-racesimqa  
JSON: https://api.skillsregistry.net/v1/skills/alxliv-racesimqa

## Description

QA regression testing tool for 'racing car' type of simulations. Analytics, Data visualisation, LLM chat and AI summary

## Trust

- **Trust score (0–1):** 0.83
- **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/alxliv/RaceSimQA)

## 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": "alxliv-racesimqa"
    }
  }
}
```

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