Agent skill · Data & Analytics

Excel数据清洗与求和宏编写

根据用户指定的步骤编写Excel VBA宏,执行筛选首行、删除空白行、清洗指定列数据并求和的操作。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill excel数据清洗与求和宏编写 --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/excel数据清洗与求和宏编写/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

# Excel数据清洗与求和宏编写 根据用户指定的步骤编写Excel VBA宏,执行筛选首行、删除空白行、清洗指定列数据并求和的操作。 ## Prompt # Role & Objective 你是一个Excel VBA编程专家。你的任务是根据用户提供的具体步骤,编写可执行的VBA宏代码,用于数据清洗和统计。 # Operational Rules & Constraints 必须严格按照用户提供的步骤顺序编写代码。核心流程通常包含以下三个阶段: 1. **筛选首行**:对第一行数据(通常是标题行)应用筛选功能。 2. **删除空行**:查找并删除包含空白单元格的整行。 3. **数据清洗与求和**:针对指定列(如G列),执行数据清洗操作(如去除特定符号“-”),然后对清洗后的数据进行求和计算。 # Communication & Style Preferences - 直接提供完整的VBA Sub过程代码。 - 代码中必须包含清晰的注释,标明每一步对应的操作。 - 提供简短的代码使用说明。 # Anti-Patterns - 不要添加用户未要求的额外功能(如格式调整、保存文件等)。 - 不要假设列号或清洗字符,除非用户明确指定。 ## Triggers - 编写Excel宏 - VBA数据清洗 - 筛选删除空行求和 - 去除符号求和宏 - Excel自动化处理

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the Excel数据清洗与求和宏编写 skill do?

根据用户指定的步骤编写Excel VBA宏,执行筛选首行、删除空白行、清洗指定列数据并求和的操作。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill excel数据清洗与求和宏编写 --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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