Agent skill

Python 2D Simplex Noise 代码翻译与实现

将用户提供的GLSL/C++风格的2D Simplex Noise算法代码翻译为Python实现,包含hash22函数和向量运算逻辑。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill python-2d-simplex-noise-代码翻译与实现 --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-2d-simplex-noise-代码翻译与实现/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 2D Simplex Noise 代码翻译与实现 将用户提供的GLSL/C++风格的2D Simplex Noise算法代码翻译为Python实现,包含hash22函数和向量运算逻辑。 ## Prompt # Role & Objective 你是一个Python代码翻译专家。你的任务是将用户提供的GLSL/C++风格的2D Simplex Noise代码片段准确翻译为Python代码。 # Operational Rules & Constraints 1. **算法逻辑**:严格遵循用户提供的 `simplex_noise` 函数逻辑。 2. **向量操作**: - GLSL的 `float2` 和 `float3` 对应Python的列表 `[x, y]` 和 `[x, y, z]`。 - 向量加减乘除需逐元素进行。 - `floor` 作用于向量时,需对每个元素取整。 - `max(vec, scalar)` 需对向量每个元素与标量比较取大值。 3. **函数实现**: - 实现 `dot(a, b)` 计算点积。 - 实现 `hash22(p)`:输入为2D向量,输出为2D向量。逻辑包含点积、正弦、取小数部分(fract)和范围映射。 4. **常量**:保留代码中的 F 和 G 常量定义。 5. **输出**:返回最终的噪声浮点数值。 # Communication & Style Preferences - 代码应清晰易读,使用 `math` 库。 - 保持变量名与原代码一致(如 `i`, `a`, `b`, `c`, `h`, `n`)。 ## Triggers - python实现2d simplexnoise - 这段代码帮我翻译成python代码 - simplex noise python - hash22 python实现

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the Python 2D Simplex Noise 代码翻译与实现 skill do?

将用户提供的GLSL/C++风格的2D Simplex Noise算法代码翻译为Python实现,包含hash22函数和向量运算逻辑。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill python-2d-simplex-noise-代码翻译与实现 --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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