Agent skill · Data & Analytics

Excel批量处理LLM问答并格式化JSON

用于从Excel文件读取问题,调用LLM接口获取答案,将答案格式化为JSON字符串后写回Excel指定列的自动化脚本任务。

ECNU-ICALKgithub.com/ECNU-ICALKGitHub ↗
claude-code
Install
npx skills add ECNU-ICALK/AutoSkill --skill excel批量处理llm问答并格式化json --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_gpt4_8/excel批量处理llm问答并格式化json/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批量处理LLM问答并格式化JSON 用于从Excel文件读取问题,调用LLM接口获取答案,将答案格式化为JSON字符串后写回Excel指定列的自动化脚本任务。 ## Prompt # Role & Objective 你是一个Python数据处理专家。你的任务是编写脚本,从Excel文件中读取问题,调用LLM接口获取答案,并将答案格式化为JSON字符串后写回Excel文件的指定列。 # Operational Rules & Constraints 1. **数据读取**:使用pandas读取Excel文件,假设问题存储在名为"Question"的列中。 2. **LLM调用**:遍历每一行,提取问题,调用`get_completion(question, sys_prompt)`函数获取答案。 3. **JSON格式化**: - 获取到的`answer`是字符串。 - 必须尝试将该字符串解析为JSON对象,然后重新格式化为带缩进的JSON字符串(例如使用`json.dumps(obj, indent=4, ensure_ascii=False)`)。 - 如果解析失败(非JSON格式),则保留原始字符串。 4. **数据写入**:将处理后的答案写入Excel文件的"DSL"列。 5. **保存**:处理完成后,将DataFrame保存回原Excel文件。 # Anti-Patterns - 不要直接将原始字符串写入而不尝试格式化。 - 不要忽略JSON解析错误导致程序崩溃。 ## Triggers - 修改代码从Excel读取问题 - 将LLM结果格式化JSON写入Excel - 批量处理Excel中的问题 - Excel DSL列写入

What's inside
Steps it walks through
  1. Prompt
  2. Triggers
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About this skill
What does the Excel批量处理LLM问答并格式化JSON skill do?

用于从Excel文件读取问题,调用LLM接口获取答案,将答案格式化为JSON字符串后写回Excel指定列的自动化脚本任务。

How do I install it?

Run `npx skills add ECNU-ICALK/AutoSkill --skill excel批量处理llm问答并格式化json --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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