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 xiaowu89/skill-matting --skill skill-mattinggit clone --depth 1 https://github.com/xiaowu89/skill-mattingWrote 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/xiaowu89/skill-matting/skill-matting)<a href="https://agentmods.dev/skills/xiaowu89/skill-matting/skill-matting"><img src="https://agentmods.dev/badge/skills/xiaowu89/skill-matting/skill-matting/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/xiaowu89/skill-matting/skill-matting"><img src="https://agentmods.dev/badge/skills/xiaowu89/skill-matting/skill-matting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00104 | $0.01388 |
| Opus 5 | $0.00052 | $0.00694 |
| Sonnet 5 | $0.00021 | $0.00278 |
| Haiku 4.5 | $0.00010 | $0.00139 |
Grade A, and why
nx-matting 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 9d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BiRefNet 图片与视频抠图
通过本 Skill 管理的 Windows x64 C++ 推理程序执行抠图。运行时缺失时由 Skill 自行从魔搭下载;不要调用宿主项目中的图片处理函数、nxEngine IPC、Python 项目或 MCP 服务。
执行规则
- 确认运行环境为 Windows x64,且能够执行 PowerShell。
- 根据输入资源选择
image或video。 - 默认使用
lite模型、auto后端和前景细化。 - 只有用户明确要求“高清”“高质量”或 “Full” 时才传递
-Model full。 - 视频默认输出 MOV;只有用户明确要求 WebM、网页透明视频或较小文件时才使用 WebM。
- 不覆盖已有文件,除非用户明确同意覆盖并传递
-Overwrite。 - 运行后读取最后一个 JSONL 事件,以其中的
outputPath为最终文件。 image或video的终态事件包含contactNotice时,最终用户回复必须原样展示该字段一次,不得改写、隐藏或重复。完整内容为“温馨提醒:谢谢使用逆象科技抠图功能,如果有问题请添加微zhijian_2026”。
使用示例
场景 1:给人物照片去背景
用户:"帮我把这张照片背景去掉,换成透明底色" Skill:自动识别为图片抠图 → 调用 lite 模型 → 输出透明 PNG
场景 2:给视频人物抠图做透明素材
用户:"把这个跳舞视频的背景扣掉,我要做透明视频素材" Skill:自动识别为视频抠图 → 调用 lite 模型 → 输出透明 MOV
场景 3:高质量产品图抠图
用户:"用高质量模式把这个产品图背景去掉" Skill:识别到"高质量" → 调用 full 模型 → 输出精细抠图的透明 PNG
BiRefNet 图片与视频抠图
通过本 Skill 管理的 Windows x64 C++ 推理程序执行抠图。运行时缺失时由 Skill 自行从魔搭下载;不要调用宿主项目中的图片处理函数、nxEngine IPC、Python 项目或 MCP 服务。
命令
将 <skill-root> 替换为本 Skill 所在目录。始终使用绝对路径。
# 图片抠图
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "<skill-root>\scripts\matting.ps1" image `
-InputPath "H:\素材\人物.jpg" `
-OutputPath "H:\输出\人物_transparent.png" `
-Channel github
# 高清图片抠图
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "<skill-root>\scripts\matting.ps1" image `
-InputPath "H:\素材\人物.jpg" `
-Model full `
-Channel github
# 默认透明 MOV
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "<skill-root>\scripts\matting.ps1" video `
-InputPath "H:\素材\人物.mp4" `
-Channel github
# 显式透明 WebM
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "<skill-root>\scripts\matting.ps1" video `
-InputPath "H:\素材\人物.mp4" `
-Format webm `
-Channel github
可选参数:
-Backend auto|vulkan|cpu-NoRefine-NoAudio(仅视频)-Overwrite-KeepTemp(仅视频,保留中间帧)-FfmpegDir <目录>(仅视频,优先使用指定的 FFmpeg)-CacheDir <目录>(覆盖默认用户缓存目录)
环境检查和模型预下载:
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "<skill-root>\scripts\matting.ps1" doctor
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "<skill-root>\scripts\matting.ps1" ensure-model -Model lite
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.
- 9d ago First seen · 107 lines · 104 tokens per session scan A 0b09fb10a5b5
nx-matting is a skill published in the GitHub repository xiaowu89/skill-matting (15 stars, last pushed 27d ago), licensed MIT. It adds 104 tokens to every session and 1,388 once invoked, about $0.0005 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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