excel-threshold-analysis-and-styling
根据 Excel 数据量级自动判断处理策略，执行数值列清洗、条件过滤，并使用 openpyxl 对符合条件的单元格进行样式标记与导出。
npx skills add OpenSenseNova/SenseNova-Skills --skill threshold-filtering --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Excel Threshold Analysis and Styling > **Note**: This sub-skill covers one step of the Excel analysis workflow. For the full pipeline (file reading, row counting, large-file optimization, export), see the parent workflow SKILL.md. Step1 读取 Excel 文件中所有工作表的行数并汇总，用于评估数据规模。 ```python import pandas as pd file_path = 'input_file.xlsx' # 读取所有 sheet 名称并统计总行数 xls = pd.ExcelFile(file_path) sheet_names = xls.sheet_names total_rows = 0 for sheet in sheet_names: # header=None 用于快速统计包含表头的总行数 df_tmp = pd.read_excel(file_path, sheet_name=sheet, header=None) rows = len(df_tmp) total_rows += rows print(f"Sheet '{sheet}': {rows} 行") print(f"\n总行数汇总: {total_rows}") ``` Step2 对目标数据表进行清洗，将指定列的非数值内容转换为缺失值并剔除，确保数据类型为数值型。 ```python target_sheet = 'Sheet1' target_col = '数量' # 待处理的目标列名 header_idx = 1 # 表头所在行索引（0开始计数） df = pd.read_excel(file_path, sheet_name=target_sheet, header=header_idx) # 强制转换数值类型，无法转换的内容变为 NaN 并删除 df[target_col] = pd.to_numeric(df[target_col], errors='coerce') df_cleaned = df.dropna(subset=[target_col]) print(f"清洗完成，有效数据行数: {len(df_cleaned)}") ``` Step3 筛选符合特定数值条件的记录并进行统计。 ```python filter_threshold = 10 df_filtered = df_cleaned[df_cleaned[target_col] > filter_threshold] print(f"{targe
What does the excel-threshold-analysis-and-styling skill do?
根据 Excel 数据量级自动判断处理策略，执行数值列清洗、条件过滤，并使用 openpyxl 对符合条件的单元格进行样式标记与导出。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill threshold-filtering --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 OpenSenseNova/SenseNova-Skills, a repository with 4,855 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.