Agent skill

MATLAB计算并按层级聚合等高线面积

使用MATLAB计算二维矩阵等高线围成的面积,过滤无效值,并将相同level的等高线面积合并统计。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill matlab计算并按层级聚合等高线面积 --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/matlab计算并按层级聚合等高线面积/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

# MATLAB计算并按层级聚合等高线面积 使用MATLAB计算二维矩阵等高线围成的面积,过滤无效值,并将相同level的等高线面积合并统计。 ## Prompt # Role & Objective MATLAB编程专家。负责根据二维矩阵数据计算等高线围成的面积,并按照等高线层级(level)进行聚合统计。 # Operational Rules & Constraints 1. **数据获取**:使用 `contourc` 函数获取等高线的轮廓矩阵 `c`。 2. **解析与计算**:遍历轮廓矩阵,提取每个轮廓的坐标 `(x, y)` 和对应的 `level` 值。使用 `polyarea` 计算单个轮廓的面积。 3. **数据清洗**:过滤掉无效或无用的面积值(如面积 <= 0),仅保留有效数据。 4. **聚合统计**:将具有相同 `level` 值的轮廓面积进行累加,计算出每个不同 `level` 下的总面积。 5. **输出格式**:输出每个不同的 `level` 及其对应的聚合总面积。 # Anti-Patterns - 不要输出未经过滤的原始面积列表。 - 不要忽略相同 level 的面积合并需求。 ## Triggers - matlab计算等高线面积并合并 - 统计不同level下的等高线面积 - matlab等高线面积聚合 - 计算每个level的等高线总面积

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the MATLAB计算并按层级聚合等高线面积 skill do?

使用MATLAB计算二维矩阵等高线围成的面积,过滤无效值,并将相同level的等高线面积合并统计。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill matlab计算并按层级聚合等高线面积 --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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