Top Value Coloring logo

Top Value Coloring

OrganizationPopular
OpenSenseNova
top-value-coloring

根据数据规模动态选择处理策略,对多表数据进行合并、统计筛选,并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。

Overview

PublisherOpenSenseNova
RepositorySenseNova-Skills
Skill nametop-value-coloring
Stars
5.6K
Forks
392
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by OpenSenseNova on GitHub. Read the source before you install it.

Installation

Install the Top Value Coloring AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills.git /tmp/SenseNova-Skills
mkdir -p .claude/skills
cp -r /tmp/SenseNova-Skills/skills/sn-da-excel-workflow/capability/excel-cell-coloring/top-value-coloring .claude/skills/top-value-coloring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Top Value Coloring in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Top Value Coloring on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Top Value Coloring is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Step1 提取并合并多个 Sheet 中的关键维度数据,进行数据清洗、类型转换及 Top-N 筛选。

python
# 示例:合并两个 Sheet 的数据
# 读取 Sheet1 并清洗
df1 = pd.read_excel(file_path, sheet_name='Sheet1', header=None)
# 假设 group_col 在第0列,value_col 在第2列
data1 = df1.iloc[20:, [0, 2]].copy()
data1.columns = ['group_col', 'value_col_1']
data1['value_col_1'] = pd.to_numeric(data1['value_col_1'], errors='coerce')
data1['group_col'] = data1['group_col'].ffill() # 处理合并单元格产生的缺失

# 读取 Sheet2 并清洗
df2 = pd.read_excel(file_path, sheet_name='Sheet2', header=None)
data2 = df2.iloc[5:, [0, 1]].copy()
data2.columns = ['value_col_2', 'value_col_3']

# 合并数据
merged_df = pd.concat([data1.reset_index(drop=True), data2.reset_index(drop=True)], axis=1)
merged_df = merged_df.dropna(subset=['value_col_1'])

# 筛选关键指标前五的数据
top_results = merged_df.nlargest(5, 'value_col_1').copy()

# 占位示例:修正特定缺失值
# top_results.loc[top_results['group_col'].isna(), 'group_col'] = 'Default_Value'

Step2 使用 openpyxl 创建格式化表格,应用条件样式(如特定列标红、最大值高亮)并设置边框与对齐方式。

python
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side

output_path = 'analysis_report.xlsx'

# 创建工作簿
wb = Workbook()
ws = wb.active
ws.title = 'Analysis_Results'

# 定义样式
header_fill = PatternFill(start_color='4472C4', end_color='4472C4', fill_type='solid')
header_font = Font(bold=True, color='FFFFFF', size=12)
red_font = Font(color='FF0000', bold=True) # 用于高亮异常或关键值
green_fill = PatternFill(start_color='C6EFCE', end_color='C6EFCE', fill_type='solid') # 用于高亮最大值
thin_border = Border(left=Side(style='thin'), right=Side(style='thin'), 
                     top=Side(style='thin'), bottom=Side(style='thin'))
center_align = Alignment(horizontal='center', vertical='center')

# 写入表头
headers = ['Rank'] + list(top_results.columns)
for col, header in enumerate(headers, 1):
    cell = ws.cell(row=1, column=col, value=header)
    cell.font = header_font
    cell.fill = header_fill
    cell.alignment = center_align
    cell.border = thin_border

# 写入数据并应用样式
for idx, (_, row) in enumerate(top_results.iterrows(), 2):
    # 写入排名
    ws.cell(row=idx, column=1, value=idx-1).border = thin_border
    
    # 写入各列数据
    for col_idx, value in enumerate(row, 2):
        cell = ws.cell(row=idx, column=col_idx, value=value)
        cell.border = thin_border
        
        # 逻辑高亮示例:对特定列(如第4列)应用红色字体
        if col_idx == 4:
            cell.font = red_font
        
        # 逻辑高亮示例:对超过阈值的值应用绿色填充
        # if isinstance(value, (int, float)) and value > threshold_val:
        #     cell.fill = green_fill

# 自动调整列宽
column_widths = {'A': 8, 'B': 30, 'C': 15, 'D': 15, 'E': 18}
for col, width in column_widths.items():
    ws.column_dimensions[col].width = width

# 设置数字格式
for row in range(2, ws.max_row + 1):
    ws.cell(row=row, column=3).number_format = '#,##0'
    ws.cell(row=row, column=4).number_format = '#,##0.00'

wb.save(output_path)
print(f"Formatted file saved to: {output_path}")

Step3 生成并输出结果文件的下载链接。

python
# 必须使用 sandbox:/ 前缀生成下载链接
print(f"[下载分析结果]({f'sandbox:{output_path}'})")

Frequently asked questions

What does the Top Value Coloring AI skill do?

根据数据规模动态选择处理策略,对多表数据进行合并、统计筛选,并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。

Why use Top Value Coloring on TypingMind?

Because you install it once and use it with any model. Top Value Coloring is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Top Value Coloring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-da-excel-workflow/capability/excel-cell-coloring/top-value-coloring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Top Value Coloring?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Top Value Coloring?

As many as you like. As long as a model supports skills, you can use Top Value Coloring with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Top Value Coloring AI skill free?

Yes. It is published on GitHub by OpenSenseNova under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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