excel-sheet-filter-export
动态统计多Sheet Excel文件行数以判断大文件处理逻辑，并根据特定条件筛选数据、重命名字段后导出为包含下载链接的新Excel文件，适用于多Sheet数据探查与条件过滤导出场景。
npx skills add OpenSenseNova/SenseNova-Skills --skill single-sheet-export --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 读取目标Sheet，清理字段格式并根据特定条件筛选记录，统计关键指标。 ```python target_sheet = 'Sheet1' # 替换为实际sheet名 df_target = pd.read_excel(file_path, sheet_name=target_sheet) # 清理目标列的字符串格式（去除首尾空格） filter_col = 'group_col' if filter_col in df_target.columns: df_target[filter_col] = df_target[filter_col].astype(str).str.strip() # 筛选符合条件的记录 target_value = 'target_value_example' mask = df_target[filter_col] == target_value df_filtered = df_target[mask] # 统计特定范围的种类数量 target_col = 'target_col' if target_col in df_filtered.columns: specific_ranges = df_filtered[target_col].dropna().unique() print(f"{target_col} 种类数量:", len(specific_ranges)) # 统计各分类数量与占比 value_counts_df = df_filtered[target_col].value_counts().reset_index() value_counts_df.columns = [target_col, '数量'] value_counts_df['占比'] = (value_counts_df['数量'] / value_counts_df['数量'].sum()).map('{:.2%}'.format) # 添加总计行 total_row = pd.DataFrame({ target_col: ['总计'], '数量': [value_counts_df['数量'].sum()], '占比': ['100.00%'] }) value_counts_df = pd.concat([value_counts_df, total_row], ignore_index=True) print(f"\
- Skill Steps
What does the excel-sheet-filter-export skill do?
动态统计多Sheet Excel文件行数以判断大文件处理逻辑，并根据特定条件筛选数据、重命名字段后导出为包含下载链接的新Excel文件，适用于多Sheet数据探查与条件过滤导出场景。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill single-sheet-export --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.