// Professional rules for AI-driven data modeling and creation. Use this skill when users need to create and manage MySQL databases, design data models using Mermaid ER diagrams, and implement database schemas.
| name | data-model-creation |
| description | Professional rules for AI-driven data modeling and creation. Use this skill when users need to create and manage MySQL databases, design data models using Mermaid ER diagrams, and implement database schemas. |
| alwaysApply | false |
Use this skill for MySQL database modeling and creation when you need to:
Do NOT use for:
Follow the modeling workflow
Apply generation rules strictly
Use tools correctly
mermaidDiagram parameter with complete mermaid classDiagram codepublish to false initially, create then publish separatelyupdateMode for new or existing modelsAs an expert in data modeling and a senior architect in software development, you are proficient in Mermaid. Your main task is to provide model structures in mermaid classDiagram format based on user descriptions, following the detailed rules below:
Type Mapping Priority: When user-described fields match the mapping relationship, prioritize using type as the field type. Mapping relationships are as follows:
| Business Field | type |
|---|---|
| Text | string |
| Number | number |
| Boolean | boolean |
| Enum | x-enum |
| Phone | phone |
| URL | url |
| File | x-file |
| Image | x-image |
| Rich Text | x-rtf |
| Region | x-area-code |
| Time | time |
| Date | date |
| DateTime | datetime |
| Object | object |
| Array | string[] |
| Location | x-location |
Naming Convention: Convert Chinese descriptions to English naming (except enum values). Use PascalCase for class names, camelCase for field names.
Field Visibility: Use default visibility for fields, do not add "+" or "-".
Array Types: When descriptions include array types, use specific array formats such as string[], number[], x-rtf[], etc.
Chinese Administrative Regions: When involving Chinese administrative regions like "province/city/district", use x-area-code field type.
Required Fields: When descriptions explicitly mention required fields, define a required() parameterless function, return value as string array of required field names, e.g., required() ["name", "age"]. By default, fields are not required.
Unique Fields: When descriptions explicitly mention unique fields, define a unique() parameterless function, return value as string array of unique field names, e.g., unique() ["name", "age"]. By default, fields are not unique.
Default Values: When descriptions explicitly require field default values, use "= default value" format after field definition, e.g., age: number = 0. By default, fields have no default values.
Field Descriptions: For each field definition in user descriptions, use <<description>> format at the end of the definition line, e.g., name: string <<Name>>.
Display Field: Each entity class should have a field for display when being referenced. Usually a human-readable name or unique identifier. Define display_field() parameterless function, return value is a field name representing the main display field, e.g., display_field() "name" means the main display field is name. Otherwise, default to the implicit _id of the data model.
Class Notes: After all class definitions are complete, use note to describe class names. First use "%% Class naming" to anchor the area, then provide Chinese table names for each class.
Relationships: When descriptions contain relationships, relationship label LabelText should not use original semantics, but use relationship field names. For example, A "n" <-- "1" B: field1 means A has many-to-one relationship with B, data exists in A's field1 field. Refer to examples for specifics.
Naming: Field names and descriptions in Mermaid should be concise and accurately expressed.
Complexity Control: Unless user requires, control complexity, e.g., number of classes should not exceed 5, control field complexity.
classDiagram
class Student {
name: string <<Name>>
age: number = 18 <<Age>>
gender: x-enum = "Male" <<Gender>>
classId: string <<Class ID>>
identityId: string <<Identity ID>>
course: Course[] <<Courses>>
required() ["name"]
unique() ["name"]
enum_gender() ["Male", "Female"]
display_field() "name"
}
class Class {
className: string <<Class Name>>
display_field() "className"
}
class Course {
name: string <<Course Name>>
students: Student[] <<Students>>
display_field() "name"
}
class Identity {
number: string <<ID Number>>
display_field() "number"
}
%% Relationships
Student "1" --> "1" Identity : studentId
Student "n" --> "1" Class : student2class
Student "n" --> "m" Course : course
Student "n" <-- "m" Course : students
%% Class naming
note for Student "Student Model"
note for Class "Class Model"
note for Course "Course Model"
note for Identity "Identity Model"
string โ VARCHAR/TEXTnumber โ INT/BIGINT/DECIMALboolean โ BOOLEAN/TINYINTdate โ DATEdatetime โ DATETIMEtime โ TIMEx-enum โ ENUM typex-file/x-image โ File path storagex-rtf โ LONGTEXT rich textx-area-code โ Region codex-location โ Geographic location coordinatesemail/phone/url โ VARCHAR with validationmermaidDiagram: Complete mermaid classDiagram codepublish: Whether to publish model immediately (recommend default to false, create then publish)updateMode: Create new model or update existing modelclassDiagram
class User {
username: string <<Username>>
email: email <<Email>>
password: string <<Password>>
avatar: x-image <<Avatar>>
status: x-enum = "active" <<Status>>
required() ["username", "email"]
unique() ["username", "email"]
enum_status() ["active", "inactive", "banned"]
display_field() "username"
}
classDiagram
class Product {
name: string <<Product Name>>
price: number <<Price>>
description: x-rtf <<Product Description>>
images: x-image[] <<Product Images>>
category: string <<Category>>
stock: number = 0 <<Stock>>
required() ["name", "price"]
display_field() "name"
}
class Order {
orderNo: string <<Order Number>>
totalAmount: number <<Total Amount>>
status: x-enum = "pending" <<Order Status>>
createTime: datetime <<Create Time>>
required() ["orderNo", "totalAmount"]
unique() ["orderNo"]
enum_status() ["pending", "paid", "shipped", "completed", "cancelled"]
display_field() "orderNo"
}
classDiagram
class Article {
title: string <<Title>>
content: x-rtf <<Content>>
author: string <<Author>>
publishTime: datetime <<Publish Time>>
status: x-enum = "draft" <<Status>>
tags: string[] <<Tags>>
required() ["title", "content", "author"]
enum_status() ["draft", "published", "archived"]
display_field() "title"
}
These rules will guide AI Agents to generate high-quality, business-requirement-compliant data models during the data modeling process.