dummy-dataset

Generate customizable CSV, JSON, SQL, or Python datasets with realistic patterns.

Updated Mar 30, 2026

Search Tags:#json #sql #csv #generate #test-data #data-generation #dummy-dataset

Ready to run

npx skills add https://github.com/omeragaakbas/zoyare --skill dummy-dataset-omeragaakbas

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dummy-dataset
Source: https://github.com/omeragaakbas/zoyare/tree/main/.claude/skills/dummy-dataset
Command: npx skills add https://github.com/omeragaakbas/zoyare --skill dummy-dataset-omeragaakbas

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creating realistic, constraint-aware test data is time-consuming and error-prone, and teams often lack quick ways to produce consistent sample datasets for development, QA, and demos.

Core Features & Use Cases

Customizable Schemas: Define columns, types, ranges, and relational rules for tables and records.
Multiple Output Formats: Export as CSV, JSON, SQL INSERTs, or an executable Python generator script.
Realistic Patterns & Constraints: Apply skewed distributions, domain-specific value pools, timestamp ranges, and cross-field business rules for believable mock data.
Use Case: Populate a staging database with realistic customer feedback records for load testing and UX demos.

Quick Start

Generate a 500-row customer feedback CSV with realistic names, emails, timestamps, categories, and a skewed rating distribution.

Frequently Asked Questions about dummy-dataset

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

FAQPage Schema

How do I generate realistic test data with custom schemas and skewed distributions?▼

To generate realistic test data, define custom column schemas, types, ranges, and relational rules, then apply skewed distributions and domain-specific value pools to produce constraint-aware mock datasets for development and QA.

Can I export generated dummy datasets directly as SQL INSERTs or JSON files?▼

Yes, you can export generated dummy datasets directly as CSV, JSON, SQL INSERTs, or executable Python generator scripts to easily populate staging databases and test environments.

What is the best way to populate a staging database with constraint-aware mock records?▼

The best way to populate a staging database with constraint-aware mock records is to define relational integrity rules and business constraints, generating realistic data that satisfies cross-field dependencies and functional requirements.

Does this test data generator support cross-field business rules and relational integrity?▼

Yes, this test data generator supports cross-field business rules and relational integrity, allowing you to apply custom constraints, timestamp ranges, and realistic value patterns for believable mock data.

Can I generate a 500-row customer feedback dataset with specific column types and rating distributions?▼

Yes, you can generate a 500-row customer feedback dataset by defining specific columns like names, emails, timestamps, and categories, while applying a skewed distribution to the rating column for realistic results.

When do I need an executable Python generator script for test data generation?▼

You need an executable Python generator script for test data generation when you want a reproducible, customizable output format to repeatedly populate staging environments or run automated QA load testing.