QC12 ConstantFieldCheck logo

QC12 ConstantFieldCheck

Organization
cas-bigdatalab
QC12_ConstantFieldCheck

特定字段恒定下的其他字段不一致检查工具。读取被检验表,当某些字段(恒定字段)的值相同时, 检查其他指定字段(差异字段)是否存在多个不同的值,如果存在则标记为异常数据,最后输出为相同格式的文件。 当用户提到恒定字段检查、字段一致性检查、分组内差异检查等需求时使用此skill。 即使用户没有说出"恒定字段",只要任务涉及检查分组内字段是否存在不一致,就应该使用此skill。

Overview

Publishercas-bigdatalab
Repositorypiflow
Skill nameQC12_ConstantFieldCheck
Stars
541
Forks
172
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cas-bigdatalab on GitHub. Read the source before you install it.

Installation

Install the QC12 ConstantFieldCheck 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.
    https://github.com/cas-bigdatalab/piflow/tree/master/workspace/skills/QC12_ConstantFieldCheck
  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/cas-bigdatalab/piflow.git /tmp/piflow
mkdir -p .claude/skills
cp -r /tmp/piflow/workspace/skills/QC12_ConstantFieldCheck .claude/skills/QC12_ConstantFieldCheck
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable QC12 ConstantFieldCheck 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 QC12 ConstantFieldCheck 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 QC12 ConstantFieldCheck 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.

QC12_ConstantFieldCheck 恒定字段一致性检查Skill

功能概述

本skill用于检查当特定字段(恒定字段)的值相同时,其他指定字段(差异字段)是否存在多个不同的值。如果存在不一致,则标记为异常数据。

触发条件

当用户请求以下任务时,应使用此skill:

  • 恒定字段检查
  • 字段一致性检查
  • 分组内差异检查
  • 检查分组内字段是否一致
  • 检查相同条件下的字段是否相同

处理逻辑

  1. 读取输入的被检验数据
  2. 解析恒定字段列表和差异字段列表
  3. 按恒定字段进行分组
  4. 检查每个分组内差异字段的唯一值数量
  5. 如果差异字段存在多个不同值,标记该分组的所有数据为异常
  6. 为异常数据添加质控标识
  7. 输出处理后的数据文件

支持的文件格式

  • CSV (.csv)
  • TSV (.tsv)
  • Excel (.xls, .xlsx)
  • SPSS (.sav)

使用方法

步骤1:获取用户输入输出路径及参数

向用户确认:

  1. 输入文件路径
  2. 输出文件路径
  3. 恒定字段名(逗号分隔,按这些字段进行分组)
  4. 差异字段名(逗号分隔,检查这些字段是否存在不一致)
  5. 质控标识
  6. 质控标识字段名(默认:QC0000)
  7. 唯一ID字段名(默认:ID0000)

步骤2:执行恒定字段一致性检查脚本

在 scripts/ 目录下找到 QC12_ConstantFieldCheck.py,使用Python执行:

bash
python scripts/QC12_ConstantFieldCheck.py \
    --input_path <输入文件> \
    --output_path <输出文件> \
    --constantFieldsNames "<恒定字段>" \
    --diffFieldsNames "<差异字段>" \
    --QcMark "<质控标识>" \
    --markFieldName <质控字段名> \
    --idFieldName <ID字段名>

步骤3:返回结果

告知用户处理完成。

参数说明

参数必填说明
--input_path是输入文件路径
--output_path是输出文件路径
--constantFieldsNames是恒定字段,逗号分隔(如:field1,field2)
--diffFieldsNames是差异字段,逗号分隔(如:field3,field4)
--QcMark是质控标识
--markFieldName否质控标识字段名,默认QC0000
--idFieldName否唯一ID字段名,默认ID0000

使用示例

假设有以下数据:

IDregiontypevalue
1A1100
2A1200
3B2150

如果设置:

  • 恒定字段:region
  • 差异字段:value

则检查结果:region=A的分组中,value有100和200两个不同值,存在不一致,标记为异常。

输出示例

[OK] Using encoding GB2312 successfully read CSV file
Successfully read file: /path/to/input.csv, shape: (1000, 18)
Processing completed! Output saved to: /path/to/output.csv

注意事项

  1. 恒定字段用于数据分组,相同值的记录被分为一组
  2. 检查差异字段在同一分组内是否存在多个不同的值
  3. 如果分组内差异字段存在多个不同值,该分组的所有记录都会被标记为异常
  4. 质控标识会追加到原有的质控标识字段中,用分号分隔

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the QC12 ConstantFieldCheck AI skill do?

特定字段恒定下的其他字段不一致检查工具。读取被检验表,当某些字段(恒定字段)的值相同时, 检查其他指定字段(差异字段)是否存在多个不同的值,如果存在则标记为异常数据,最后输出为相同格式的文件。 当用户提到恒定字段检查、字段一致性检查、分组内差异检查等需求时使用此skill。 即使用户没有说出"恒定字段",只要任务涉及检查分组内字段是否存在不一致,就应该使用此skill。

Why use QC12 ConstantFieldCheck on TypingMind?

Because you install it once and use it with any model. QC12 ConstantFieldCheck 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 QC12 ConstantFieldCheck in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cas-bigdatalab/piflow/tree/master/workspace/skills/QC12_ConstantFieldCheck. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use QC12 ConstantFieldCheck?

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 QC12 ConstantFieldCheck?

As many as you like. As long as a model supports skills, you can use QC12 ConstantFieldCheck with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the QC12 ConstantFieldCheck AI skill free?

It is published on GitHub by cas-bigdatalab. Check the repository for licensing terms. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇