large-file-conditional-formatting SKILL.md skillA
large-file-conditional-formatting 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/large-file-conditional-formatting@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-cell-coloring/threshold-cell-coloring/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260426.f0ab496, committed Apr 26, 2026
- Size
- 4,435 bytes
- Repository stars
- 6,031
- License
- MIT
根据Excel总行数自动切换Parquet加速读取，计算特定维度的时间序列平均值，并使用openpyxl输出带有条件格式（如低于均值标绿）和自定义样式的分析报告。.
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
## Skill Steps
> **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 读取文件并统计所有 sheet 的行数，汇总后打印总行数，用于判断是否需要大文件加速。
```python
import pandas as pd
import openpyxl
file_path = "input_data.xlsx"
# 获取所有sheet名称
wb = openpyxl.load_workbook(file_path, read_only=True)
sheet_names = wb.sheetnames
print("Sheet列表:", sheet_names)
print("Sheet数量:", len(sheet_names))
# 统计每个sheet的行数
total_rows = 0
for name in sheet_names:
df_temp = pd.read_excel(file_path, sheet_name=name, header=None)
rows = len(df_temp)
total_rows += rows
print(f"Sheet '{name}': {rows} 行")
print(f"\n总行数 = {total_rows}")
```
Step2 提取目标实体的时间序列数据，计算平均值，并构建包含比较结果的结构化 DataFrame。
```python
target_entity = 'Target_Entity' # 占位示例，如 'US'
# 提取目标行数据 (假设第0列为实体名称)
target_row = df[df[0] == target_entity]
# 提取时间标签和对应数值 (假设第6行为表头，1:10列为数据)
time_labels = df.iloc[6, 1:10].tolist()
target_values = target_row.iloc[0, 1:10].tolist()
target_values_numeric = [float(v) for v in target_values]
# 计算平均值
avg_value = sum(target_values_numeric) / len(target_values_numeric)
# 构建结果 DataFrame
…Read the whole file at its exact version.
How to install
mdr add opensensenova/sensenova-skills/large-file-conditional-formatting@sha256:539ded0021ba3acfThe 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_jaq7q2zqc5yatiji)
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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_jaq7q2zqc5yatiji GET https://markdownregistry.com/api/v1/resolve?ref=opensensenova/sensenova-skills/large-file-conditional-formatting GET https://markdownregistry.com/api/v1/blob/539ded0021ba3acf2fdc758bd65ead3f303e3e023019c2a3ad021be56bd9168c
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.