top-value-coloring
根据数据规模动态选择处理策略，对多表数据进行合并、统计筛选，并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。
npx skills add OpenSenseNova/SenseNova-Skills --skill top-value-coloring --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.
Step1 提取并合并多个 Sheet 中的关键维度数据，进行数据清洗、类型转换及 Top-N 筛选。 ```python # 示例：合并两个 Sheet 的数据 # 读取 Sheet1 并清洗 df1 = pd.read_excel(file_path, sheet_name='Sheet1', header=None) # 假设 group_col 在第0列，value_col 在第2列 data1 = df1.iloc[20:, [0, 2]].copy() data1.columns = ['group_col', 'value_col_1'] data1['value_col_1'] = pd.to_numeric(data1['value_col_1'], errors='coerce') data1['group_col'] = data1['group_col'].ffill() # 处理合并单元格产生的缺失 # 读取 Sheet2 并清洗 df2 = pd.read_excel(file_path, sheet_name='Sheet2', header=None) data2 = df2.iloc[5:, [0, 1]].copy() data2.columns = ['value_col_2', 'value_col_3'] # 合并数据 merged_df = pd.concat([data1.reset_index(drop=True), data2.reset_index(drop=True)], axis=1) merged_df = merged_df.dropna(subset=['value_col_1']) # 筛选关键指标前五的数据 top_results = merged_df.nlargest(5, 'value_col_1').copy() # 占位示例：修正特定缺失值 # top_results.loc[top_results['group_col'].isna(), 'group_col'] = 'Default_Value' ``` Step2 使用 openpyxl 创建格式化表格，应用条件样式（如特定列标红、最大值高亮）并设置边框与对齐方式。 ```python from openpyxl import Workbook from openpyxl.styles import Font, PatternFill, Alignment, Border, Side output_path = 'analysis_report.xlsx' # 创建工作簿 wb = Workbook() ws = wb.active ws.title = 'Analysis_Results' # 定义样式 header_fil
What does the top-value-coloring skill do?
根据数据规模动态选择处理策略，对多表数据进行合并、统计筛选，并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill top-value-coloring --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 4,911 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.