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。
Install
$ agentstack add skill-opensensenova-sensenova-skills-threshold-filtering ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-opensensenova-sensenova-skills-threshold-filtering) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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。
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。
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。
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
- Source: OpenSenseNova/SenseNova-Skills
- License: MIT
Install and usage instructions live in the source repository linked above.
Reviews
No reviews yet, be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.