Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add KhazixW2/Everything-Maa --skill collect-game-item-datagit clone --depth 1 https://github.com/KhazixW2/Everything-MaaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/khazixw2/everything-maa/collect-game-item-data)<a href="https://agentmods.dev/skills/khazixw2/everything-maa/collect-game-item-data"><img src="https://agentmods.dev/badge/skills/khazixw2/everything-maa/collect-game-item-data/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/khazixw2/everything-maa/collect-game-item-data"><img src="https://agentmods.dev/badge/skills/khazixw2/everything-maa/collect-game-item-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.01035 |
| Opus 5 | $0.00024 | $0.00517 |
| Sonnet 5 | $0.00010 | $0.00207 |
| Haiku 4.5 | $0.00005 | $0.00103 |
Grade A, and why
collect-game-item-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
collect-game-item-data
收集游戏物品数据的自动化流程,使用 maa-mcp 工具操作。
触发条件
用户要求收集游戏中的物品数据(如装备、武器、道具等)时使用此skill。
工作流程
1. 设备连接
mcp__maa-mcp__find_adb_device_list() → 选择设备 → mcp__maa-mcp__connect_adb_device(device_name)
2. 进入物品列表
mcp__maa-mcp__ocr(controller_id)- 识别当前屏幕,找到入口按钮坐标mcp__maa-mcp__click(controller_id, x, y)- 点击进入物品类别(如"武器")mcp__maa-mcp__wait(seconds=1.5)- 等待界面加载mcp__maa-mcp__ocr(controller_id)- 确认已进入物品列表页
3. 遍历收集物品详情
对列表中的每个物品:
mcp__maa-mcp__click(controller_id, x, y)- 点击物品进入详情页mcp__maa-mcp__wait(seconds=1.5)- 等待详情加载mcp__maa-mcp__ocr(controller_id)- 识别详情页数据- 提取关键字段:名称、类型、属性、描述、获取途径等
mcp__maa-mcp__click(controller_id, x, y)- 点击X按钮关闭 或mcp__maa-mcp__click_key(controller_id, key=4)- 使用返回键mcp__maa-mcp__wait(seconds=1.5)- 等待返回列表mcp__maa-mcp__ocr(controller_id)- 确认已返回列表,继续下一个
4. 数据写入JSON
{
"item_category": "物品类别",
"collection_date": "YYYY-MM-DD",
"items": [
{
"name": "物品名称",
"type": "类型",
"属性1": 值,
"属性2": 值,
"source": "获取途径",
"description": "描述"
}
]
}
使用 Write 工具写入文件。
OCR结果字段说明
mcp__maa-mcp__ocr() 返回格式:
[{
"box": [x, y, width, height],
"score": 0.99,
"text": "识别文字"
}]
box[0]: 左上角X坐标box[1]: 左上角Y坐标box[2]: 宽度box[3]: 高度- 中心点坐标:
x + width/2, y + height/2
常用操作
| 操作 | 工具 |
|---|---|
| 截图OCR | mcp__maa-mcp__ocr(controller_id) |
| 单击 | mcp__maa-mcp__click(controller_id, x, y) |
| 双击 | mcp__maa-mcp__double_click(controller_id, x, y) |
| 滑动 | mcp__maa-mcp__swipe(controller_id, start_x, start_y, end_x, end_y, duration) |
| 返回键 | mcp__maa-mcp__click_key(controller_id, key=4) |
| 等待 | mcp__maa-mcp__wait(seconds) |
物品列表坐标推算
列表布局通常是网格或纵向排列,可根据OCR结果的 box 坐标推算下一个物品位置。
注意事项
- 点击前先确认坐标在物品名称范围内
- 关闭详情页优先使用X按钮,其次使用返回键(key=4)
- 等待时间可根据游戏加载速度调整(通常1.5-2秒)
- 复杂游戏可能需要滚动(swipe)列表才能看到所有物品
示例命令
/collect-game-item-data
用户描述:
收集装备栏下面武器栏的所有武器数据,包括名称、类型、攻击、命中率、需求力量、重量、获取途径。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 111 lines · 48 tokens per session scan A 48856be8cf76
collect-game-item-data is a skill published in the GitHub repository KhazixW2/Everything-Maa (12 stars, last pushed 3d ago), licensed MIT. It adds 48 tokens to every session and 1,035 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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