Extract key metrics from spreadsheet data, check whether recorded values match calculations after unit conversion, and rank selected metrics from highest to lowest. Export the ranking or validation results to an Excel file for download.
中文简介正在准备中。当前显示英语简介。
根据数据量自动选择读取策略（大文件转Parquet），提取关键指标进行单位一致性验证与排序分析，并输出可下载的结果表格。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.
Step1 提取关键指标，进行物理量/指标的单位一致性验证计算，并对核心业务指标进行降序排列。
# 1. 物理量/指标单位一致性验证与计算 (保留公式结构示例)
col_numerator = 'numerator_col' # 示例：Mx (kN·m)
col_denominator = 'denominator_col' # 示例：Wx (cm³)
col_target = 'target_col' # 示例：sigma (MPa)
if col_numerator in data.columns and col_denominator in data.columns and col_target in data.columns:
# 单位换算示例：统一到标准单位后计算
data['den_converted'] = data[col_denominator] * 1e-6
data['num_converted'] = data[col_numerator] * 1e3
data['calc_result_pa'] = data['num_converted'] / data['den_converted']
data['calc_result_mpa'] = data['calc_result_pa'] / 1e6
# 容差验证
tolerance = 1e-6
data['is_valid'] = abs(data['calc_result_mpa'] - data[col_target]) < tolerance
print("单位一致性验证通过率:", data['is_valid'].mean() * 100, "%")
# 2. 提取关键指标并降序排列
group_col = 'group_col' # 示例：开发区名称
metric_col = 'metric_col' # 示例：实际到帐外资额
result_df = pd.DataFrame()
if group_col in data.columns and metric_col in data.columns:
result_df = data[[group_col, metric_col]].copy()
result_df = result_df.sort_values(metric_col, ascending=False).reset_index(drop=True)
Step2 将分析与验证结果整理为最终的数据框，保存为 Excel 文件，并生成可供下载的链接。
output_path = 'analysis_result.xlsx'
# 确定最终输出的数据框
if not result_df.empty:
result_df_final = result_df
elif 'calc_result_mpa' in data.columns:
result_df_final = data[[col_numerator, col_denominator, col_target, 'calc_result_mpa', 'is_valid']].copy()
result_df_final.columns = ['分子指标', '分母指标', '目标比对值', '计算结果', '是否一致']
else:
result_df_final = data.head(100) # 默认输出前100行作为示例
# 保存为Excel文件
result_df_final.to_excel(output_path, index=False, engine='openpyxl')
print(f"分析结果已保存至: {output_path}")
# 生成下载链接
print(f"下载链接: [点击下载分析结果](./{output_path})")
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Compare counts and shares for two categories in Excel data. The skill cleans the selected columns, calculates differences and percentages, and produces a summary spreadsheet plus bar and pie charts.
中文简介正在准备中。当前显示英语简介。
Analyze Excel data using custom categories, count distributions, and compare groups with charts. Score text using its length, technical terms, numbers, and selected complex concepts, then visualize difficulty levels and the relationship between text length and scores.
中文简介正在准备中。当前显示英语简介。
Count categories in a chosen data column after removing missing values and placeholder labels. Calculate each category’s share and create high-resolution charts combining counts, percentages, pie charts, and horizontal bars for distribution reports.
中文简介正在准备中。当前显示英语简介。
Clean category and numeric fields in structured data, compare groups with counts and cross-tabulations, and produce charts, Excel results, and a Markdown report. Export visuals as high-resolution PNG and SVG files for use in reports.
中文简介正在准备中。当前显示英语简介。
Filter Excel data using candidate column names, RGB color values, text prefixes, and regular expressions, which match text patterns. Calculate totals and averages, then export results to Excel with highlighted rows and a download link.
中文简介正在准备中。当前显示英语简介。
Filter rows from Excel or previously converted Parquet files, a format designed for efficient data storage. Summarize matching records by group with counts, percentages, and totals, then export the summary to Excel.
中文简介正在准备中。当前显示英语简介。