large-file-parquet-analysis-and-highlight

by OpenSenseNova5.8k

当Excel文件总行数超过1万行时，通过转换为Parquet格式提升读取性能，提取目标指标并计算最大值，最后将结果输出为Excel并对特定行进行高亮标注。

Install

The skill file stays in the publisher's repository.

Paste the copied command in a terminal. It installs this SKILL.md into that agent.

npx skills add OpenSenseNova/SenseNova-Skills --skill category-coloring -a cursor -y

Download skill skills/sn-da-excel-workflow/capability/excel-cell-coloring/category-coloring/SKILL.md on GitHub

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Questions

How do I install large-file-parquet-analysis-and-highlight?: Download skills/sn-da-excel-workflow/capability/excel-cell-coloring/category-coloring/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does large-file-parquet-analysis-and-highlight live?: The publisher keeps it at https://github.com/OpenSenseNova/SenseNova-Skills.
Which agents can use large-file-parquet-analysis-and-highlight?: Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.

name: large-file-parquet-analysis-and-highlight description: "当Excel文件总行数超过1万行时，通过转换为Parquet格式提升读取性能，提取目标指标并计算最大值，最后将结果输出为Excel并对特定行进行高亮标注。"

Skill Steps

Step1 读取文件并统计所有 sheet 的行数，汇总后打印总行数，用于判断数据规模是否需要启用大文件处理。

import pandas as pd

file_path = "input_data.xlsx"

# 读取所有sheet并统计总行数
xls = pd.ExcelFile(file_path)
sheet_names = xls.sheet_names
print(f"Sheet列表: {sheet_names}")

total_rows = 0
for sheet in sheet_names:
    # 仅读取一列以加快行数统计速度
    df_temp = pd.read_excel(file_path, sheet_name=sheet, usecols=[0], header=None)
    rows = len(df_temp)
    total_rows += rows
    print(f"Sheet '{sheet}': {rows} 行")

print(f"\n总行数 = {total_rows}")