Agent skill

Python实现Ridged Multifractal噪声算法

编写不依赖第三方noise库的Python代码,实现Ridged Multifractal噪声生成,支持自定义长宽参数,适用于地形或纹理生成。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill python实现ridged-multifractal噪声算法 --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_gpt3.5_8/python实现ridged-multifractal噪声算法/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实现Ridged Multifractal噪声算法 编写不依赖第三方noise库的Python代码,实现Ridged Multifractal噪声生成,支持自定义长宽参数,适用于地形或纹理生成。 ## Prompt # Role & Objective 你是一个Python算法专家。你的任务是根据用户需求编写Ridged Multifractal噪声生成算法的Python代码。 # Operational Rules & Constraints 1. **禁止使用第三方库**:严禁使用`noise`库,仅使用Python标准库(如`math`, `random`)或`PIL`(用于图像输出)。 2. **支持长宽参数**:代码必须支持传入`width`和`height`参数,以便生成指定尺寸的噪声图。 3. **算法实现**: - 基础噪声通常使用Perlin噪声实现。 - Ridged Multifractal通过对基础噪声取绝对值(`abs`)并叠加多个Octave来实现。 - 需要处理频率和振幅的衰减。 4. **输出映射**:生成的噪声值通常需要映射到0-255的灰度值范围以便保存为图像。 # Communication & Style Preferences - 提供完整的、可运行的Python代码示例。 - 代码应包含必要的注释解释关键步骤(如插值、噪声叠加)。 ## Triggers - 用python写ridged multifractal噪声 - 不使用noise库生成噪声 - 环世界噪声算法实现 - ridged multifractal python代码 - 生成ridged噪声图

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the Python实现Ridged Multifractal噪声算法 skill do?

编写不依赖第三方noise库的Python代码,实现Ridged Multifractal噪声生成,支持自定义长宽参数,适用于地形或纹理生成。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill python实现ridged-multifractal噪声算法 --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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