excel-conditional-filtering-optimization SKILL.md skillA
excel-conditional-filtering-optimization from opensensenova/sensenova-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add opensensenova/sensenova-skills/excel-conditional-filtering-optimization@git:20260426.f0ab496Pins git:20260426.f0ab496 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- opensensenova/sensenova-skills
- Path
skills/sn-da-excel-workflow/capability/excel-data-filtering/range-filtering/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260426.f0ab496, committed Apr 26, 2026
- Size
- 2,704 bytes
- Repository stars
- 6,031
- License
- MIT
根据多维数值条件筛选 Excel 数据并导出结果，支持大规模数据的自动性能优化处理。.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Excel_Conditional_Filtering_Optimization
> **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数据，统计各表行数并汇总，用于评估数据规模。
```python
import pandas as pd
file_path = "input_data.xlsx"
# 读取所有 sheet，统计行数
xls = pd.ExcelFile(file_path)
print("Sheet names:", xls.sheet_names)
total_rows = 0
sheet_details = []
for sheet in xls.sheet_names:
df_temp = pd.read_excel(file_path, sheet_name=sheet)
row_count = len(df_temp)
sheet_details.append({"sheet": sheet, "rows": row_count})
total_rows += row_count
print(f"Sheet details: {sheet_details}")
print(f"Total rows across all sheets: {total_rows}")
```
Step2 对目标数据进行清洗，处理表头偏移，并将关键列转换为数值类型以确保计算准确。
```python
# 读取目标数据表
target_sheet = 'Sheet1'
df = pd.read_excel(file_path, sheet_name=target_sheet, header=0)
# 处理可能的子表头或空行偏移（示例：跳过第一行）
# df = df.iloc[1:].reset_index(drop=True)
# 统一设置列名（根据实际业务逻辑调整占位符）
# df.columns = ['col_1', 'col_2', 'col_3', 'target_id', 'val_a', 'val_b', 'val_c']
# 强制转换数值列，处理非数值数据为 NaN
numeric_cols = ['val_a', 'val_b', 'val_c', 'target_id']
…Read the whole file at its exact version.
How to install
mdr add opensensenova/sensenova-skills/excel-conditional-filtering-optimization@sha256:328a3c732d4317a0The line at the top of this page pins the label git:20260426.f0ab496, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_5kx2756pibvzatua)
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Versions
The registry has recorded 1 version of this file, committed on Apr 26, 2026.
Audit of the latest version
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Source
opensensenova/sensenova-skills · 6,031 stars · license MIT · pushed 2026-11-03 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_5kx2756pibvzatua GET https://markdownregistry.com/api/v1/resolve?ref=opensensenova/sensenova-skills/excel-conditional-filtering-optimization GET https://markdownregistry.com/api/v1/blob/328a3c732d4317a083bd0220f5d4d74ada7a2b73e8206b2628198db858c510d6
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.