# Sn Da Excel Workflow

Excel 数据分析多步编排器。覆盖：(1) 读取多 Sheet Excel 文件并统计行数，(2) 大文件检测（≥10k 行自动 Parquet 优化），(3)...

- Repository: https://github.com/OpenSenseNova/SenseNova-Skills (6002 stars)
- Category: Documents
- License: MIT
- Last updated: 2026-10-29
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add OpenSenseNova/SenseNova-Skills --skill sn-da-excel-workflow -g -a claude-code -y`
Codex: `npx skills add OpenSenseNova/SenseNova-Skills --skill sn-da-excel-workflow -g -a codex -y`
Cursor: `npx skills add OpenSenseNova/SenseNova-Skills --skill sn-da-excel-workflow -g -a cursor -y`
Gemini CLI: `npx skills add OpenSenseNova/SenseNova-Skills --skill sn-da-excel-workflow -g -a gemini-cli -y`

## Description

Excel 数据分析多步编排器。覆盖：(1) 读取多 Sheet Excel 文件并统计行数，(2) 大文件检测（≥10k 行自动 Parquet 优化），(3) 数据清洗（缺失值、文本标准化、无效字符），(4) 条件筛选与分类提取，(5) 跨 Sheet 统计聚合，(6) 导出 Excel/CSV 并提供下载链接。覆盖从数据读取到报告生成全流程，按步骤编排 capability 子 skill。**遇到以下任一情况就主动使用本 skill，不要自行写几行 pandas 就回答**：①用户出现触发词：Excel 分析 / 表格分析 / 数据分析 / 数据清洗 / 数据统计 / 数据筛选 / 数据可视化 / 数据导出 / 汇总统计 / 透视表 / 分组统计 / 交叉分析 / 趋势分析 / 对比分析 / 异常值检测 / 去重 / 缺失值处理 / Excel 报告 / 生成报表 / analyze Excel / data analysis / data cleaning / pivot table；②用户上传或指定了 .xlsx / .xls / .csv 文件并要求分析、清洗、统计或可视化；③任务涉及多 Sheet 读取、条件筛选、分类汇总、图表生成中的任意一项；④用户要求导出带格式的 Excel 报告或下载链接。仅不用于：不涉及表格数据的纯文本处理、图片分析（使用 sn-da-image-caption）、单个公式计算的简单问答。

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Excel Data Analysis Workflow

End-to-end workflow for structured Excel analysis. Each step maps to a
capability sub-skill that can be loaded for detailed patterns.

## Workflow

### Step 1, Count rows across all sheets (lightweight, no full load)

Count rows per sheet **without loading data into memory**. Use openpyxl
`read_only` mode, this works for any file size.

```python
import openpyxl, gc

wb = openpyxl.load_workbook(file_path, read_only=True, data_only=True)
total_rows = 0
sheet_info = {}
for name in wb.sheetnames:
    ws = wb[name]
    row_count = sum(1 for _ in ws.iter_rows(min_row=2, values_only=True))
    total_rows += row_count
    sheet_info[name] = row_count
    print(f"Sheet '{name}': {row_count} rows")
wb.close()
print(f"总行数={total_rows}")
```

⚠️ **Do NOT use `pd.read_excel()` to count rows**, it loads all data into
memory, which will OOM on large files.

→ capability: `excel-reading/multi-sheet-reading`

### Step 2, Large file gate (CRITICAL, choose strategy by row count)

| total_rows | Strategy | What to do |
|-----------|----------|------------|
| < 10k | Direct read | `df = pd.read_excel(file_path, sheet_name=target_sheet)` |
| 10k, 100k | Parquet cache | `pd.read_excel()` once → `df.to_parquet()` → all later reads from Parquet |
| **>= 100k** | **STOP. Load `sn-da-large-file-analysis` skill** | Read its SKILL.md, then follow its streaming read + Parquet pattern. **Do NOT use `pd.read_excel()` at all**, it will OOM or timeout on 100k+ rows. |

**For >= 100k rows:**
```
read_file(path="<skills_base>/sn-da-large-file-analysis/SKILL.md")
```
Then use `stream_excel_to_parquet()` from that skill, it reads via
openpyxl `iter_rows` in 50k-row chunks with constant memory.

**For 10k, 100k rows (only):**
```python
import pandas as pd
parquet_path = "/tmp/_auto_parquet.parquet"
df = pd.read_excel(file_path, sheet_name=target_sheet)
df.to_parquet(parquet_path, engine="pyarrow")
del df; gc.collect()
df = pd.read_parquet(parquet_path)
```

The rest: https://github.com/OpenSenseNova/SenseNova-Skills/blob/HEAD/skills/sn-da-excel-workflow/SKILL.md