When to use this skill

This is an OPTIONAL advanced modeling tool for complex database design. Most simple table creation should use

relational-database-tool

directly with SQL statements.

ONLY use this skill when you specifically need:

Complex multi-table relationships with automatic foreign key management
Visual ER diagram generation for documentation
Automated field type mapping and constraint generation
Enterprise-level data model documentation

For most cases, use
rules/relational-database-tool/rule.md
instead:

Simple table creation with CREATE TABLE statements
Basic CRUD operations
Schema modifications with ALTER TABLE
Direct SQL execution without Mermaid modeling

Do NOT use for:

Querying or manipulating existing data (use database skills)
NoSQL database design (use NoSQL skills)
Frontend data structures (use appropriate frontend skills)

How to use this skill (for a coding agent)

⚠️ NOTE: This is OPTIONAL. For simple tasks, skip this and use
relational-database-tool
directly.

When you do use this advanced modeling approach:

Optional modeling workflow (only when complexity justifies it)
- Business analysis phase: Analyze user requirements, identify core entities and relationships
- Mermaid modeling phase: Create mermaid classDiagram following generation rules
- Model validation phase: Check completeness, consistency, and correctness
Apply generation rules strictly (when using this tool)
- Use correct type mappings (string, number, boolean, x-enum, etc.)
- Convert Chinese to English naming (PascalCase for classes, camelCase for fields)
- Define required(), unique(), display_field() functions when needed
- Use proper relationship notation with field names
Use tools correctly (only when you choose this approach)
- Call data model creation tools only for complex multi-entity business requirements
- Use
```
mermaidDiagram
```
  parameter with complete mermaid classDiagram code
- Set
```
publish
```
  to false initially, create then publish separately
- Choose appropriate
```
updateMode
```
  for new or existing models

Quick Decision Guide

Most Database Tasks →
rules/relational-database-tool/rule.md

✅ Simple table creation
✅ Data queries and modifications
✅ Schema changes
✅ Direct SQL execution

Complex Modeling Only → This rule (
rules/data-model-creation/rule.md
)

🎯 Multi-entity relationship modeling
🎯 Automated foreign key management
🎯 Visual ER diagram generation
🎯 Enterprise documentation

Data Model AI Modeling Professional Rules

⚠️ IMPORTANT: Simplified Workflow Recommendation

For most database table creation tasks, use
rules/relational-database-tool/rule.md
directly:

Simple table creation:

CREATE TABLE users (id INT PRIMARY KEY, name VARCHAR(255))

Schema modifications:

ALTER TABLE users ADD COLUMN email VARCHAR(255)

Data operations:
```
INSERT
```
,
```
UPDATE
```
,
```
SELECT
```
,
```
DELETE
```

Only use this advanced Mermaid modeling approach when:

You need automated relationship management
Complex multi-table schemas with foreign keys
Enterprise documentation requirements
Visual ER diagram generation

This rule exists for complex modeling scenarios, but most development should use direct SQL execution.

AI Modeling Expert Prompt

As 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:

Generation Rules

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
Email	email
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.

Standard Example

mermaid

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"

Data Model Creation Workflow

1. Business Analysis Phase

Carefully analyze user's business requirement descriptions
Identify core entities and business objects
Determine relationships between entities
Clarify required fields, unique constraints, and default values

2. Mermaid Modeling Phase

Strictly follow the above generation rules to create mermaid classDiagram
Ensure field type mappings are correct
Properly handle relationship directions and cardinalities
Add complete Chinese descriptions and comments

3. Model Validation Phase

Check model completeness and consistency
Verify relationship rationality
Confirm field constraint correctness
Check naming convention compliance

MySQL Data Type Support

Basic Type Mappings

```
string
```
→ VARCHAR/TEXT
```
number
```
→ INT/BIGINT/DECIMAL
```
boolean
```
→ BOOLEAN/TINYINT
```
date
```
→ DATE
```
datetime
```
→ DATETIME
```
time
```
→ TIME

Extended Type Mappings

```
x-enum
```
→ ENUM type
```
x-file
```
/
```
x-image
```
→ File path storage
```
x-rtf
```
→ LONGTEXT rich text
```
x-area-code
```
→ Region code
```
x-location
```
→ Geographic location coordinates
```
email
```
/
```
phone
```
/
```
url
```
→ VARCHAR with validation

Relationship Implementation

One-to-one: Foreign key constraints
One-to-many: Foreign key associations
Many-to-many: Intermediate table implementation
Self-association: Same table foreign key

Tool Usage Guidelines

Tool Call Timing (RARE - Use Sparingly)

Only when user explicitly requests advanced data modeling with Mermaid diagrams
Only for complex enterprise applications with multi-entity relationships
Only when user provides detailed business requirement descriptions requiring automated modeling
Only when you need to update existing data model structure AND want visual ER diagrams

When to SKIP this tool (Most Cases)

Simple table creation → Use
```
executeWriteSQL
```
with CREATE TABLE
Schema changes → Use
```
executeWriteSQL
```
with ALTER TABLE
Basic CRUD → Use appropriate SQL statements directly
Data queries → Use
```
executeReadOnlySQL
```

Parameter Usage Guide

```
mermaidDiagram
```
: Complete mermaid classDiagram code
```
publish
```
: Whether to publish model immediately (recommend default to false, create then publish)
```
updateMode
```
: Create new model or update existing model

Error Handling Strategy

Syntax errors: Check Mermaid syntax format
Field type errors: Verify type mapping relationships
Relationship errors: Check relationship directions and cardinalities
Naming conflicts: Provide renaming suggestions

Best Practices

Model Design Principles

Single Responsibility: Each entity class is responsible for only one business concept
Minimize Dependencies: Reduce unnecessary relationships
Extensibility: Reserve field space for future expansion
Consistency: Maintain consistency in naming and type usage

Performance Considerations

Index Design: Create indexes for commonly queried fields
Field Length: Reasonably set string field lengths
Relationship Optimization: Avoid excessive many-to-many relationships
Data Sharding: Consider table sharding strategies for large tables

Security Standards

Sensitive Fields: Encrypt storage for sensitive information like passwords
Permission Control: Clarify read/write permissions for fields
Data Validation: Set appropriate field constraints
Audit Logs: Add operation records for important entities

Common Business Scenario Templates

User Management System

mermaid

classDiagram
    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"
    }

E-commerce System

mermaid

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"
    }

Content Management System

mermaid

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.

data-model-creation

NPX Install

SKILL.md Content

When to use this skill

How to use this skill (for a coding agent)

Quick Decision Guide

Data Model AI Modeling Professional Rules

⚠️ IMPORTANT: Simplified Workflow Recommendation

AI Modeling Expert Prompt

Generation Rules

Standard Example

Data Model Creation Workflow

1. Business Analysis Phase

2. Mermaid Modeling Phase

3. Model Validation Phase

MySQL Data Type Support

Basic Type Mappings

Extended Type Mappings

Relationship Implementation

Tool Usage Guidelines

Tool Call Timing (RARE - Use Sparingly)

When to SKIP this tool (Most Cases)

Parameter Usage Guide

Error Handling Strategy

Best Practices

Model Design Principles

Performance Considerations

Security Standards

Common Business Scenario Templates

User Management System

E-commerce System

Content Management System