excel-multi-sheet-dynamic-analysis
用于分析包含多个Sheet的Excel文件，动态判断数据量级以决定是否转换为Parquet进行大文件处理，并支持跨Sheet的特定字段统计、数据清洗、交叉分析与可视化，最终生成带下载链接的汇总报告。
npx skills add OpenSenseNova/SenseNova-Skills --skill specific-sheet-reading --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 遍历所有sheet，灵活定位目标列并统计特定类型字段的数量。 ```python target_col_keyword = 'type' # 占位示例 target_val_keyword = 'varchar' # 占位示例 total_target_count = 0 target_details = [] for sheet_name in wb.sheetnames: ws = wb[sheet_name] raw_data = list(ws.iter_rows(values_only=True)) # 实用技巧：灵活策略定位目标列，通过扫描前几行数据内容定位表头行 header_row_idx = None for i, row in enumerate(raw_data): if any(cell and isinstance(cell, str) and target_col_keyword in str(cell).lower() for cell in row): header_row_idx = i break if header_row_idx is not None: header = raw_data[header_row_idx] type_col_idx = next((j for j, col in enumerate(header) if col and target_col_keyword in str(col).lower()), None) if type_col_idx is not None: target_count = 0 target_fields = [] for i in range(header_row_idx + 1, len(raw_data)): row = raw_data[i] if len(row) <= type_col_idx: continue cell_val = row[type_col_idx] if cell_val and isinstance(cell_val, str) and target_val_keyword in cell_val.lower(): target_count += 1 field_name = row[0] if len(row) > 0 else None if field_name and field_name not in target_fields: target_fields.append(field_name) total_target_count += target_count target_details.append({ 'sheet': sheet_name, 'target_count': target_coun
What does the excel-multi-sheet-dynamic-analysis skill do?
用于分析包含多个Sheet的Excel文件，动态判断数据量级以决定是否转换为Parquet进行大文件处理，并支持跨Sheet的特定字段统计、数据清洗、交叉分析与可视化，最终生成带下载链接的汇总报告。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill specific-sheet-reading --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.