install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/dataverse-schema-design" ~/.claude/skills/majiayu000-claude-skill-registry-dataverse-schema-design && rm -rf "$T"
manifest:
skills/data/dataverse-schema-design/SKILL.mdsource content
dataverse-schema-design
This skill provides guidance on designing Dataverse table schemas and data models. Use when users ask about "Dataverse table design", "Dataverse schema", "Dataverse relationships", "Dataverse columns", "data modeling Dataverse", "Dataverse best practices", or need help designing their data structure.
Table Design Fundamentals
Naming Conventions
- Table prefix: Use publisher prefix (e.g.,
,new_
)cr123_ - Table names: PascalCase, singular (e.g.,
,new_Project
)new_Task - Column names: prefix_columnname (e.g.,
,new_projectname
)new_startdate
Create Table with SDK
# Basic table client.create_table("new_Project", { "new_ProjectName": "string", # Text column "new_Description": "string", # Multi-line text "new_Budget": "decimal", # Currency/decimal "new_StartDate": "datetime", # Date and time "new_IsActive": "bool", # Yes/No "new_Priority": Priority # Choice/enum })
Column Types
Text Columns
# Single line (max 4000 chars) "new_Name": "string" # Multi-line text (memo) "new_Description": "string" # Will be nvarchar(max)
Number Columns
# Integer "new_Quantity": "int" # Decimal (with precision) "new_Amount": "decimal" # Default precision # Currency (use decimal with formatting) "new_Budget": "decimal"
Date/Time Columns
# Date and time "new_StartDate": "datetime" # Date only (format in app) "new_DueDate": "datetime"
Boolean Columns
# Yes/No "new_IsActive": "bool" "new_IsApproved": "bool"
Choice (Picklist) Columns
from enum import IntEnum class Status(IntEnum): DRAFT = 1 SUBMITTED = 2 APPROVED = 3 REJECTED = 4 class Priority(IntEnum): LOW = 1 MEDIUM = 2 HIGH = 3 # Create table with choices client.create_table("new_Request", { "new_Name": "string", "new_Status": Status, "new_Priority": Priority })
Relationship Patterns
One-to-Many (1:N)
Parent table has many child records.
Account (1) ←→ (N) Contact └── An account has many contacts Project (1) ←→ (N) Task └── A project has many tasks
Implementation:
- Create lookup column on child table
- Reference parent using
@odata.bind
# Create contact linked to account contact = { "firstname": "John", "lastname": "Doe", "parentcustomerid_account@odata.bind": f"/accounts({account_id})" } client.create("contact", contact)
Many-to-Many (N:N)
Records in both tables can relate to multiple records in the other.
Account (N) ←→ (N) Contact └── Contacts can be related to multiple accounts
Note: N:N relationships require creating an intersection entity via the UI or advanced API calls.
Self-Referential
Table references itself (e.g., employee hierarchy).
Employee ├── new_ManagerId → Employee └── Parent employee record
Schema Design Best Practices
Do's
- Plan before creating - Design schema on paper first
- Use meaningful names - Clear, descriptive column names
- Add descriptions - Document purpose of each column
- Set required fields - Enforce data quality
- Use appropriate types - Don't store numbers as text
- Create indexes - On frequently filtered columns
- Use lookups - Instead of duplicating data
Don'ts
- Don't over-normalize - Balance between normalization and performance
- Don't use reserved names - Avoid system column names
- Don't create wide tables - Split into related tables if >100 columns
- Don't store calculated data - Use calculated columns instead
- Don't ignore security - Plan field-level security early
Common Schema Patterns
Master-Detail
Account (Master) ├── new_AccountNumber ├── new_Name └── Contacts (Detail) ├── new_FirstName ├── new_LastName └── _parentcustomerid_value (FK)
Status Workflow
new_Request ├── new_Name ├── new_Status (Draft → Submitted → Approved/Rejected) ├── new_SubmittedOn ├── new_ApprovedBy └── new_ApprovedOn
Audit Trail
new_Order ├── new_OrderNumber ├── new_Status ├── createdon (system) ├── createdby (system) ├── modifiedon (system) └── modifiedby (system)
Adding Columns to Existing Tables
# Add new columns client.create_columns("new_Project", { "new_CompletionPercentage": "int", "new_ActualEndDate": "datetime" }) # Remove columns client.delete_columns("new_Project", ["new_OldColumn"])
References
- See
for detailed patternsreferences/table-design.md - See
for relationship examplesreferences/relationships.md - See
for optimization tipsreferences/performance.md