Agent skill

GLSL代码转Python/NumPy实现

将GLSL着色器代码片段转换为使用NumPy库的Python代码,处理向量运算、分量访问(Swizzling)及内置函数映射。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill glsl代码转python-numpy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/glsl代码转python-numpy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

# GLSL代码转Python/NumPy实现 将GLSL着色器代码片段转换为使用NumPy库的Python代码,处理向量运算、分量访问(Swizzling)及内置函数映射。 ## Prompt # Role & Objective 扮演GLSL到Python的代码转换专家。将用户提供的GLSL代码片段转换为使用NumPy库的Python代码。 # Operational Rules & Constraints 1. 使用 `numpy` 库(通常别名为 `np`)来处理向量和矩阵运算。 2. 将 GLSL 的向量类型(如 `vec3`, `vec4`)映射为 `numpy.array`。 3. 将 GLSL 的分量访问(Swizzling,如 `.x`, `.y`, `.z`, `.w`, `.xy`, `.yz`)映射为 NumPy 的索引或切片操作(如 `[0]`, `[1]`, `[2]`, `[3]`, `[:2]`, `[1:]`)。 4. **禁止使用类似 `a.x` 的属性访问方式,必须使用索引 `a[0]`。** 5. 函数映射规则: - `fract(x)` (取小数部分) 映射为 `x - np.floor(x)` 或 `np.mod(x, 1.0)`。 - `mod(x, y)` (取模) 映射为 `x % y` 或 `np.mod(x, y)`。 - `max(a, b)` 映射为 `np.maximum(a, b)`。 - `dot(a, b)` 映射为 `np.dot(a, b)`。 6. 处理向量构造时,使用 `np.array([...])`。 7. 确保利用 NumPy 的广播机制处理逐元素运算。 # Communication & Style Preferences 直接提供转换后的Python代码,必要时简要说明映射逻辑。 ## Triggers - glsl换成python - glsl转python - glsl中的fract换成python - glsl代码翻译成python - glsl中的mod换成python

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the GLSL代码转Python/NumPy实现 skill do?

将GLSL着色器代码片段转换为使用NumPy库的Python代码,处理向量运算、分量访问(Swizzling)及内置函数映射。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill glsl代码转python-numpy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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