numeric-format-normalization
对 Excel 数据进行数值格式标准化与清洗，支持大规模数据的 Parquet 转换流程，并完成关键指标的合计核对与结果文件导出。
npx skills add OpenSenseNova/SenseNova-Skills --skill numeric-format-normalization --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.
## Skill Steps > This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md. Step1 对目标列进行数据清洗（去除空值、标准化数值格式），计算合计值，并与指定汇总 Sheet 中的合计行进行精确核对。 ```python target_col = '目标数值列' # 示例：'建筑面积' summary_sheet_name = 'Summary' # 示例汇总Sheet名 summary_item_col = '项目' summary_value_col = '数值' # 数据清洗：去除空值、强制转换为数值格式 df_cleaned = df_processed.dropna(subset=[target_col]).copy() df_cleaned[target_col] = pd.to_numeric(df_cleaned[target_col], errors='coerce') # 计算合计 total_calculated = df_cleaned[target_col].sum() # 从指定 Sheet 中读取“合 计”行数值进行核对 try: summary_sheet = pd.read_excel(file_path, sheet_name=summary_sheet_name) expected_total = summary_sheet.loc[summary_sheet[summary_item_col] == '合 计', summary_value_col].values[0] # 核对一致性 (处理浮点数精度问题) if abs(total_calculated - expected_total) < 1e-6: consistency = "一致" difference = 0 else: consistency = "不一致" difference = abs(total_calculated - expected_total) print(f"计算合计: {total_calculated}, 指定合计: {expected_total}, 一致性: {consistency}") except Exception as e: print(f"核对失败: {e}") expected_total = None consistency = "未知" difference = None ``` Step2 将分析与核对结果保存为表格文件，并生成可供下载的文件链接。 ```python outpu
- Skill Steps
What does the numeric-format-normalization skill do?
对 Excel 数据进行数值格式标准化与清洗，支持大规模数据的 Parquet 转换流程，并完成关键指标的合计核对与结果文件导出。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill numeric-format-normalization --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.