Data Model Creation

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 relational-database-tool skill 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:

1. 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

2. 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

3. 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 → relational-database-tool skill

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

Complex Modeling Only → This skill (data-model-creation)

• 🎯 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 relational-database-tool skill 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

1. 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

2. Naming Convention: Convert Chinese descriptions to English naming (except enum values). Use PascalCase for class names, camelCase for field names.

3. Field Visibility: Use default visibility for fields, do not add "+" or "-".

4. Array Types: When descriptions include array types, use specific array formats such as string[], number[], x-rtf[], etc.

5. Chinese Administrative Regions: When involving Chinese administrative regions like "province/city/district", use x-area-code field type.

6. 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.

7. 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.

8. 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.

9. Field Descriptions: For each field definition in user descriptions, use <<description>> format at the end of the definition line, e.g., name: string <<Name>>.

10. 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.

11. 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.

12. 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.

13. Naming: Field names and descriptions in Mermaid should be concise and accurately expressed.

14. Complexity Control: Unless user requires, control complexity, e.g., number of classes should not exceed 5, control field complexity.

Standard Example

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)

1. Only when user explicitly requests advanced data modeling with Mermaid diagrams
2. Only for complex enterprise applications with multi-entity relationships
3. Only when user provides detailed business requirement descriptions requiring automated modeling
4. 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 manageSqlDatabase(action="runStatement") with CREATE TABLE
• Schema changes → Use manageSqlDatabase(action="runStatement") with ALTER TABLE
• Basic CRUD → Use appropriate SQL statements directly
• Data queries → Use querySqlDatabase(action="runQuery")

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

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

Performance Considerations

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

Security Standards

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

Common Business Scenario Templates

User Management System

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

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

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.