QC2 EnumerationFieldCheck logo

QC2 EnumerationFieldCheck

Organization
cas-bigdatalab
QC2_EnumerationFieldCheck

公共基础项枚举校验工具。读取被检验表和标准词典文件,将被检验表中的属性项值与标准词典进行比对, 检查是否超出词典规定的固有词表枚举值范围,超出范围的记录会标记质控标识并输出异常数据。 当用户提到枚举校验、枚举值检查、属性项校验、词表校验等需求时使用此skill。 即使用户没有明确说出"枚举校验",只要任务涉及将被检验数据与标准词典进行比对,就应该使用此skill。

Overview

Publishercas-bigdatalab
Repositorypiflow
Skill nameQC2_EnumerationFieldCheck
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 QC2 EnumerationFieldCheck 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/QC2_EnumerationFieldCheck
  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/QC2_EnumerationFieldCheck .claude/skills/QC2_EnumerationFieldCheck
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable QC2 EnumerationFieldCheck 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 QC2 EnumerationFieldCheck 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 QC2 EnumerationFieldCheck 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.

QC2_EnumerationFieldCheck 枚举校验Skill

功能概述

本skill用于将被检验表中的属性项值与相应标准词典进行比对,检查是否超出词典规定的固有词表枚举值范围。如果超出范围,会标记质控标识并输出异常数据。

触发条件

当用户请求以下任务时,应使用此skill:

  • 枚举校验
  • 枚举值检查
  • 属性项校验
  • 词表校验
  • 检查数据是否在标准词典范围内
  • 数据枚举值验证

处理逻辑

  1. 读取被检验表和标准词典文件
  2. 根据指定的对比字段进行匹配(支持多字段)
  3. 找出不在标准词典范围内的异常数据
  4. 为异常数据添加质控标识
  5. 输出两个文件:
    • 异常数据文件
    • 处理后的原始数据文件(带质控标识)

输入文件说明

被检验表 (origin_file_path)

需要校验的数据文件,支持CSV、TSV、Excel、SPSS等格式。

标准词典表 (standard_file_path)

标准词典文件,包含正确的枚举值列表。

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. 被检验表路径
  2. 标准词典文件路径
  3. 异常数据输出路径
  4. 处理后原始数据输出路径
  5. 对比字段(格式:原始字段:标准字段,如:sscode,ssname:sscode,ssname)
  6. 质控标识(如:QC2_ERROR)
  7. 质控标识字段名(默认:QC0000)
  8. 唯一ID字段名(默认:ID0000)

步骤2:执行枚举校验脚本

在 scripts/ 目录下找到 QC2_EnumerationFieldCheck.py,使用Python执行:

bash
python scripts/QC2_EnumerationFieldCheck.py \
    --origin_file_path <被检验表> \
    --standard_file_path <标准词典> \
    --error_output_path <异常数据输出> \
    --origin_output_path <处理后数据输出> \
    --comparison_field "<对比字段>" \
    --qc_mark "<质控标识>" \
    --mark_field_name <质控字段名> \
    --id_field_name <ID字段名>

步骤3:返回结果

  • 如果有异常数据:显示异常数量并输出异常数据预览
  • 如果无异常:告知用户校验通过

参数说明

参数必填说明
--origin_file_path是被检验数据文件路径
--standard_file_path是标准词典文件路径
--error_output_path是异常数据输出路径
--origin_output_path是处理后原始数据输出路径
--comparison_field是对比字段,格式:原字段1,原字段2:标准字段1,标准字段2
--qc_mark是质控标识(如QC2_ERROR)
--mark_field_name否质控标识字段名,默认QC0000
--id_field_name否唯一ID字段名,默认ID0000

输出示例

有异常数据

[OK] Using encoding GB2312 successfully read CSV file
Successfully read file: /path/to/origin.csv, shape: (1000, 18)
Successfully read file: /path/to/standard.csv, shape: (50, 2)
########## Exception: Enumeration value out of range!!!
Abnormal data (first 10 rows):
...
Enumeration check completed, output files generated!

无异常数据

[OK] Using encoding GB2312 successfully read CSV file
Successfully read file: /path/to/origin.csv, shape: (1000, 18)
Successfully read file: /path/to/standard.csv, shape: (50, 2)
Enumeration check completed, output files generated!

注意事项

  1. 对比字段格式必须正确,使用冒号分隔原字段和标准字段
  2. 输出两个文件:异常数据和处理后的原始数据
  3. 异常数据的质控标识字段会被标记为用户指定的质控标识
  4. 处理后的原始数据会保留原始质控标识并追加新的标识

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 QC2 EnumerationFieldCheck AI skill do?

公共基础项枚举校验工具。读取被检验表和标准词典文件,将被检验表中的属性项值与标准词典进行比对, 检查是否超出词典规定的固有词表枚举值范围,超出范围的记录会标记质控标识并输出异常数据。 当用户提到枚举校验、枚举值检查、属性项校验、词表校验等需求时使用此skill。 即使用户没有明确说出"枚举校验",只要任务涉及将被检验数据与标准词典进行比对,就应该使用此skill。

Why use QC2 EnumerationFieldCheck on TypingMind?

Because you install it once and use it with any model. QC2 EnumerationFieldCheck 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 QC2 EnumerationFieldCheck in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cas-bigdatalab/piflow/tree/master/workspace/skills/QC2_EnumerationFieldCheck. 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 QC2 EnumerationFieldCheck?

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 QC2 EnumerationFieldCheck?

As many as you like. As long as a model supports skills, you can use QC2 EnumerationFieldCheck with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the QC2 EnumerationFieldCheck 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 👇