# AttentionOS

> AttentionOS — zhonghaozhan-attentionos. Use this tool when you need to assess and manage human attention span, providing a 60-second attention test and local-first observability. It solves problems related to focus and productivity, offering a desktop pet companion and attention state management for agents. The tool accepts user interactions and outputs attention state data, ideal for use in contexts where focus and concentration are crucial.

Canonical page: https://skillsregistry.net/skills/zhonghaozhan-attentionos  
JSON: https://api.skillsregistry.net/v1/skills/zhonghaozhan-attentionos

## Description

60s attention span test + desktop pet + attention://state MCP for agents. Local-first human attention observability. 60秒注意力测试 · 桌宠 · 注意力观测

## Trust

- **Trust score (0–1):** 0.75
- **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/zhonghaozhan/AttentionOS)

## 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": "zhonghaozhan-attentionos"
    }
  }
}
```

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