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dataverse-datagenerator

Generate and import realistic test data into Dataverse tables.

Updated Apr 16, 2026
Search Tags:#data generation#test data#web api#dataverse#schema discovery
One-click install
npx skills add https://github.com/jaduplesms/fieldservice-accelerator --skill dataverse-datagenerator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dataverse-datagenerator
Source: https://github.com/jaduplesms/fieldservice-accelerator/tree/main/skills/dataverse-datagenerator
Command: npx skills add https://github.com/jaduplesms/fieldservice-accelerator --skill dataverse-datagenerator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires msal, requests, microsoft-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill solves the problem of creating realistic test data for Dataverse tables without relying on real-world data, which helps in ensuring data privacy and regulatory compliance.

Core Features & Use Cases

  • Data Generation: Automatically generate test data based on table schemas in Dataverse.
  • Schema Discovery: Discovers table schemas and reference data from live metadata and Microsoft Learn docs.
  • Data Import: Imports generated data using PAC CLI and the Dataverse Web API with upsert logic to avoid duplicates.

Quick Start

Generate and import realistic test data for your Dataverse table by executing the command: ```bash ./scripts/deploy.py

Frequently Asked Questions about dataverse-datagenerator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate realistic test data for Dataverse tables?▼

To generate test data for Dataverse tables, this Skill automates schema discovery from live metadata and Microsoft Learn docs, creates realistic records using Python libraries, and imports them via the Dataverse Web API and PAC CLI.

What is the best way to populate a Dataverse table with test data without using real records?▼

The best way to populate Dataverse tables without real records is using an automated Python script that generates realistic data based on discovered schemas and imports it through PAC CLI with upsert logic to prevent duplicates.

How does schema discovery work when creating test data for Dataverse?▼

Schema discovery for Dataverse test data works by querying live table metadata and referencing Microsoft Learn documentation to automatically understand table structures before generating matching realistic records.

Do I need PAC CLI to import generated data into Dataverse?▼

Yes, you need PAC CLI to import the generated test data into Dataverse tables, as the Skill relies on it alongside the Dataverse Web API to execute upsert logic and ensure data integrity during the import process.

Can I use Python libraries to automate Dataverse test data generation and import?▼

Yes, you can use Python libraries like requests and msal to automate Dataverse test data generation and import, handling everything from schema discovery to upsert logic via the Web API.

Why does my Dataverse data import create duplicate records?▼

Dataverse data imports create duplicate records when not using upsert logic; this Skill prevents duplicates by applying upsert logic during the import process through the Dataverse Web API and PAC CLI.

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