# AWorld-Runtime

> AWorld-Runtime — qingw-dev-aworld-runtime. Use this tool when you need to scale agent training and accelerate development workflows. AWorld-Runtime solves problems of inefficient training processes and limited parallelization, enabling faster and more efficient agent development. It takes in runtime definitions and outputs optimized training workflows, ideal for use cases where large-scale agent training is required.

Canonical page: https://skillsregistry.net/skills/qingw-dev-aworld-runtime  
JSON: https://api.skillsregistry.net/v1/skills/qingw-dev-aworld-runtime

## Description

Scaling Agent Training via Runtime Definitions and Parallelizations

## Trust

- **Trust score (0–1):** 0.17
- **Verification tier:** scanned
- **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/qingw-dev/AWorld-Runtime)

## 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": "qingw-dev-aworld-runtime"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/qingw-dev-aworld-runtime` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/qingw-dev-aworld-runtime/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