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Choi-Keith
Choi-KeithCOMMUNITY
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dummy-dataset

Generate realistic mock datasets as CSV, JSON, SQL, or Python scripts.

Updated Aug 10, 2026
Search Tags:#python#json#sql#test data#csv#mock data#dataset generation
One-click install
npx skills add https://github.com/Choi-Keith/skill-arsenal-ultra --skill dummy-dataset-choi-keith
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dummy-dataset
Source: https://github.com/Choi-Keith/skill-arsenal-ultra/tree/main/plugins/pm-skills/pm-execution/skills/dummy-dataset
Command: npx skills add https://github.com/Choi-Keith/skill-arsenal-ultra --skill dummy-dataset-choi-keith

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Developers and product teams often need realistic test data to validate features, populate staging environments, or demo products, but hand-writing sample records is slow and produces unrealistic data. ## Core Features & Use Cases - Custom Schema Definition: Specify columns, data types, value ranges, and row counts for any dataset type such as customer feedback, transactions, or user profiles. - Multiple Output Formats: Produce CSV files, JSON structures, SQL INSERT statements, or executable Python generator scripts. - Business Constraints: Apply realistic rules like rating distributions, category-rating correlations, and valid email domains. - Use Case: You are building a feedback analytics dashboard and need 500 rows of customer feedback with a 40% five-star skew where bug reports only appear in low ratings. This Skill generates a ready-to-use CSV or Python script matching those constraints. ## Quick Start Ask the AI to generate a 200-row customer feedback dataset with rating, category, and email columns as a CSV file, with bug categories limited to 1-3 star ratings.

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 for my application?▼

Define your dataset type, columns with data types and value ranges, row count, and output format. The generator produces records following realistic patterns like valid email formats, plausible names, and dates within a specified window.

What output formats can mock datasets be exported to?▼

Datasets can be exported as CSV for spreadsheets and databases, JSON for APIs and NoSQL stores, SQL INSERT statements for relational databases, or as a standalone Python script you can rerun and customize.

Can I apply business rules to generated test data?▼

Yes. You can specify constraints such as rating distribution skews, category-to-rating correlations, and domain restrictions. For example, bug reports can be restricted to 1-3 star ratings while feature requests appear only in 3-5 star ratings.

How many rows of dummy data can I generate?▼

The default is 100 rows, but you can set any row count via the ROWS parameter. For very large datasets, generating a Python script is recommended so you can regenerate or scale the data on demand.

When should I use a Python generator script instead of a static CSV?▼

Use a Python script when you need to regenerate data repeatedly, customize generation logic later, or produce very large datasets. A static CSV or SQL file is better for one-off imports into databases or spreadsheets.

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