Numeric Format Normalization

OrganizationPopular

OpenSenseNova

numeric-format-normalization

对 Excel 数据进行数值格式标准化与清洗，支持大规模数据的 Parquet 转换流程，并完成关键指标的合计核对与结果文件导出。

Overview

Publisher	OpenSenseNova
Repository	`SenseNova-Skills`
Skill name	`numeric-format-normalization`
Stars	5.6K
Forks	392
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 Numeric Format Normalization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/OpenSenseNova/SenseNova-Skills/tree/main/skills/sn-da-excel-workflow/capability/excel-data-cleaning/numeric-format-normalization

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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-data-cleaning/numeric-format-normalization .claude/skills/numeric-format-normalization

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Numeric Format Normalization 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 Numeric Format Normalization 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 Numeric Format Normalization 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.

Skill Steps

This sub-skill covers one capability of the Excel workflow. For reading/counting/Parquet optimization, see the parent workflow SKILL.md.

Step1 对目标列进行数据清洗（去除空值、标准化数值格式），计算合计值，并与指定汇总 Sheet 中的合计行进行精确核对。

python
target_col = '目标数值列'  # 示例：'建筑面积'
summary_sheet_name = 'Summary' # 示例汇总Sheet名
summary_item_col = '项目'
summary_value_col = '数值'

# 数据清洗：去除空值、强制转换为数值格式
df_cleaned = df_processed.dropna(subset=[target_col]).copy()
df_cleaned[target_col] = pd.to_numeric(df_cleaned[target_col], errors='coerce')

# 计算合计
total_calculated = df_cleaned[target_col].sum()

# 从指定 Sheet 中读取“合 计”行数值进行核对
try:
    summary_sheet = pd.read_excel(file_path, sheet_name=summary_sheet_name)
    expected_total = summary_sheet.loc[summary_sheet[summary_item_col] == '合 计', summary_value_col].values[0]
    
    # 核对一致性 (处理浮点数精度问题)
    if abs(total_calculated - expected_total) < 1e-6:
        consistency = "一致"
        difference = 0
    else:
        consistency = "不一致"
        difference = abs(total_calculated - expected_total)
    
    print(f"计算合计: {total_calculated}, 指定合计: {expected_total}, 一致性: {consistency}")
except Exception as e:
    print(f"核对失败: {e}")
    expected_total = None
    consistency = "未知"
    difference = None

Step2 将分析与核对结果保存为表格文件，并生成可供下载的文件链接。

python
output_path_xlsx = 'analysis_result.xlsx'
output_path_csv = 'analysis_result.csv'

# 构建结果表格
result_data = {
    '统计项': ['总行数', f'{target_col}合计（计算值）', f'{target_col}合计（指定值）', '一致性', '差异值'],
    '数值': [total_rows, total_calculated, expected_total, consistency, difference]
}
result_df = pd.DataFrame(result_data)

# 保存为多种格式
result_df.to_excel(output_path_xlsx, index=False)
result_df.to_csv(output_path_csv, index=False, encoding='utf-8-sig')

# 输出下载链接（在报告中展示）
print("分析结果已保存，可下载：")
print(f"- [{output_path_xlsx}](sandbox:/{output_path_xlsx})")
print(f"- [{output_path_csv}](sandbox:/{output_path_csv})")

Frequently asked questions

What does the Numeric Format Normalization AI skill do?

对 Excel 数据进行数值格式标准化与清洗，支持大规模数据的 Parquet 转换流程，并完成关键指标的合计核对与结果文件导出。

Why use Numeric Format Normalization on TypingMind?

Because you install it once and use it with any model. Numeric Format Normalization 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 Numeric Format Normalization 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-data-cleaning/numeric-format-normalization. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Numeric Format Normalization?

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 Numeric Format Normalization?

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

Is the Numeric Format Normalization 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Skill Steps

Frequently asked questions

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