# Excel Threshold Analysis And Styling

> 根据 Excel 数据量级自动判断处理策略，执行数值列清洗、条件过滤，并使用 openpyxl 对符合条件的单元格进行样式标记与导出。

- **Type:** Skill
- **Install:** `agentstack add skill-opensensenova-sensenova-skills-threshold-filtering`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [OpenSenseNova](https://agentstack.voostack.com/s/opensensenova)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [OpenSenseNova](https://github.com/OpenSenseNova)
- **Source:** https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-da-excel-workflow/capability/excel-data-filtering/threshold-filtering

## Install

```sh
agentstack add skill-opensensenova-sensenova-skills-threshold-filtering
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Excel Threshold Analysis and Styling

> **Note**: This sub-skill covers one step of the Excel analysis workflow. For the full pipeline (file reading, row counting, large-file optimization, export), see the parent workflow SKILL.md.

Step1 读取 Excel 文件中所有工作表的行数并汇总，用于评估数据规模。
```python
import pandas as pd

file_path = 'input_file.xlsx'

# 读取所有 sheet 名称并统计总行数
xls = pd.ExcelFile(file_path)
sheet_names = xls.sheet_names
total_rows = 0

for sheet in sheet_names:
    # header=None 用于快速统计包含表头的总行数
    df_tmp = pd.read_excel(file_path, sheet_name=sheet, header=None)
    rows = len(df_tmp)
    total_rows += rows
    print(f"Sheet '{sheet}': {rows} 行")

print(f"\n总行数汇总: {total_rows}")
```

Step2 对目标数据表进行清洗，将指定列的非数值内容转换为缺失值并剔除，确保数据类型为数值型。
```python
target_sheet = 'Sheet1'
target_col = '数量' # 待处理的目标列名
header_idx = 1     # 表头所在行索引（0开始计数）

df = pd.read_excel(file_path, sheet_name=target_sheet, header=header_idx)

# 强制转换数值类型，无法转换的内容变为 NaN 并删除
df[target_col] = pd.to_numeric(df[target_col], errors='coerce')
df_cleaned = df.dropna(subset=[target_col])

print(f"清洗完成，有效数据行数: {len(df_cleaned)}")
```

Step3 筛选符合特定数值条件的记录并进行统计。
```python
filter_threshold = 10 
df_filtered = df_cleaned[df_cleaned[target_col] > filter_threshold]

print(f"{target_col} 大于 {filter_threshold} 的记录共有 {len(df_filtered)} 条")
```

Step4 使用 openpyxl 对原始文件中

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [OpenSenseNova](https://github.com/OpenSenseNova)
- **Source:** [OpenSenseNova/SenseNova-Skills](https://github.com/OpenSenseNova/SenseNova-Skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-opensensenova-sensenova-skills-threshold-filtering
- Seller: https://agentstack.voostack.com/s/opensensenova
- Browse the marketplace: https://agentstack.voostack.com/browse

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