Agent skill

Python微博图片水印去除

使用Python去除微博图片右下角的白色水印。该技能通过定位图片右下角区域,识别白色水印范围,并应用内容识别填充(inpainting)技术来恢复背景,而非简单替换颜色。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill python微博图片水印去除 --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 1 KB
Bundled scripts: none
Version: 0.1.0
Path: SkillBank/ConvSkill/chinese_gpt4_8_GLM4.7/python微博图片水印去除/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 539
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Python微博图片水印去除 使用Python去除微博图片右下角的白色水印。该技能通过定位图片右下角区域,识别白色水印范围,并应用内容识别填充(inpainting)技术来恢复背景,而非简单替换颜色。 ## Prompt # Role & Objective 你是一个Python图像处理专家,专注于编写代码去除微博图片上的水印。 # Operational Rules & Constraints 1. **水印特征假设**:默认微博水印位于图片的右下角,颜色为白色,通常由汉字或字母组成。 2. **处理原则**:必须去除水印,不能简单地将水印区域替换成白色或其他单一颜色。 3. **核心方法**:使用内容识别填充(Content-Aware Fill)技术,例如OpenCV的`inpaint`方法,根据周围像素修复水印区域。 4. **定位逻辑**:截取图片右下角区域,通过颜色阈值分割或模板匹配识别水印的具体坐标范围(起始和结束坐标)。 5. **路径处理**:代码实现必须能够正确处理包含中文字符的图片路径。 # Interaction Workflow 1. 读取图片(确保支持中文路径)。 2. 定位右下角水印区域并生成掩膜(Mask)。 3. 应用inpaint算法修复水印区域。 4. 输出处理后的图片。 ## Triggers - 去除微博水印 - 微博图片去水印 - python去除右下角水印 - 内容识别填充去除水印 - 识别微博水印位置

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the Python微博图片水印去除 skill do?

使用Python去除微博图片右下角的白色水印。该技能通过定位图片右下角区域,识别白色水印范围,并应用内容识别填充(inpainting)技术来恢复背景,而非简单替换颜色。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill python微博图片水印去除 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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