large-file-parquet-analysis-and-highlight SKILL.md skillA
large-file-parquet-analysis-and-highlight 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-parquet-analysis-and-highlight@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/category-coloring/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260426.f0ab496, committed Apr 26, 2026
- Size
- 3,444 bytes
- Repository stars
- 6,031
- License
- MIT
当Excel文件总行数超过1万行时，通过转换为Parquet格式提升读取性能，提取目标指标并计算最大值，最后将结果输出为Excel并对特定行进行高亮标注。.
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
Step1 读取文件并统计所有 sheet 的行数，汇总后打印总行数，用于判断数据规模是否需要启用大文件处理。
```python
import pandas as pd
file_path = "input_data.xlsx"
# 读取所有sheet并统计总行数
xls = pd.ExcelFile(file_path)
sheet_names = xls.sheet_names
print(f"Sheet列表: {sheet_names}")
total_rows = 0
for sheet in sheet_names:
# 仅读取一列以加快行数统计速度
df_temp = pd.read_excel(file_path, sheet_name=sheet, usecols=[0], header=None)
rows = len(df_temp)
total_rows += rows
print(f"Sheet '{sheet}': {rows} 行")
print(f"\n总行数 = {total_rows}")
```
Step2 当总行数 ≥ 1万时，读取已转换为 Parquet 格式的数据文件，通过行列匹配提取目标指标数据，并找出最大值及其对应分类。
```python
import pandas as pd
# 假设已通过大文件处理技能将Excel转换为Parquet
parquet_path = "converted_data.parquet"
df = pd.read_parquet(parquet_path)
# 假设第2行（索引1）是分类表头（如：控股类型、区域等）
header_row = df.iloc[1].tolist()
print("分类表头:", header_row)
# 找到目标指标所在的行（占位示例：'目标指标名称'）
target_metric = '目标指标名称'
target_rows = df[df[0] == target_metric]
if not target_rows.empty:
# 提取数值
values = target_rows.iloc[0, 1:].tolist()
# 清洗数据并找出最大值及其对应的分类
numeric_values = []
for val in values:
try:
numeric_values.append(float(val))
except:
numeric_values.append(0)
…Read the whole file at its exact version.
How to install
mdr add opensensenova/sensenova-skills/large-file-parquet-analysis-and-highlight@sha256:d6e0201155cff41eThe 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_brsppyn56t36dawb)
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Versions
The registry has recorded 1 version of this file, committed on Apr 26, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (3444 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
opensensenova/sensenova-skills · 6,031 stars · license MIT · pushed 2026-11-03 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_brsppyn56t36dawb GET https://markdownregistry.com/api/v1/resolve?ref=opensensenova/sensenova-skills/large-file-parquet-analysis-and-highlight GET https://markdownregistry.com/api/v1/blob/d6e0201155cff41e3e2bccfcd032de111291cc6fa3130c02497f1964295e0a67
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.