formatted-export-with-parquet
从多Sheet Excel文件中识别指定条件的记录，并将筛选结果以整行标红格式导出为Excel文件，适用于数据清洗、条件筛选与可视化标记场景。
npx skills add OpenSenseNova/SenseNova-Skills --skill formatted-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.
# Formatted_Export > This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md. ## Skill Steps Step1 对所有 sheet 进行扫描，通过模糊匹配定位目标列，筛选出符合条件（如空值或无效字符）的记录。 ```python empty_target_rows = [] for sheet_name, sheet_df in all_sheets.items(): target_col = None # 优先匹配目标列名（示例：包含特定关键字的列） for col in sheet_df.columns: if 'keyword1' in str(col).lower() or 'keyword2' in str(col).lower(): target_col = col break if target_col is None: # 尝试次级推断逻辑 for col in sheet_df.columns: if 'keyword3' in str(col) and ('keyword4' in str(col)): target_col = col break if target_col is None: continue # 数据清洗：筛选空值和无效字符（如空格、'nan'）行 mask = sheet_df[target_col].isna() | (sheet_df[target_col].astype(str).str.strip() == '') | (sheet_df[target_col].astype(str).str.strip() == 'nan') empty_rows = sheet_df[mask].copy() if len(empty_rows) > 0: empty_rows.insert(0, '来源Sheet', sheet_name) empty_target_rows.append(empty_rows) # 合并结果 result_df = pd.concat(empty_target_rows, ignore_index=True) if empty_target_rows else pd.DataFrame() ``` Step2 将筛选出的记录导出为 Excel 文件，整行标红显示以便于视觉识别，并生成下载链接。 ```python from openpyxl import load_workbook from openpyxl.styles import Pa
- Skill Steps
What does the formatted-export-with-parquet skill do?
从多Sheet Excel文件中识别指定条件的记录，并将筛选结果以整行标红格式导出为Excel文件，适用于数据清洗、条件筛选与可视化标记场景。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill formatted-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.