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dummy-dataset

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

1|Updated May 6, 2026
Search Tags:#python#sql#data generation#test data#csv#dummy data#mock dataset
One-click install
npx skills add https://github.com/moonzff/Codex_up --skill dummy-dataset-moonzff
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dummy-dataset
Source: https://github.com/moonzff/Codex_up/tree/main/skills/docs-meetings-writing/dummy-dataset
Command: npx skills add https://github.com/moonzff/Codex_up --skill dummy-dataset-moonzff

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Creating realistic test data by hand is slow and error-prone, and developers often need sample datasets that respect business rules, valid formats, and plausible value distributions for testing, demos, and environment seeding. ## Core Features & Use Cases - Multi-Format Output: Produces datasets as CSV files, JSON structures, SQL INSERT statements, or executable Python generator scripts. - Constraint-Aware Generation: Applies business rules such as rating distributions, category-rating relationships, and realistic email domains. - Customizable Schemas: Lets you define dataset type, columns, row counts, and value ranges for domains like customer feedback, transactions, or user profiles. - Use Case: You need 500 rows of customer feedback data where bug reports only appear with low ratings and 40% of entries are 5-star reviews; the Skill generates a ready-to-use CSV or Python script matching those constraints. ## Quick Start Ask the assistant to generate a 200-row customer feedback dataset with rating constraints and export it as a CSV file.

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, row count, and constraints, then generate output as CSV, JSON, SQL, or a Python script. The generator applies realistic patterns like valid email formats, plausible date ranges, and business-rule-compliant value distributions.

What output formats can dummy dataset generation produce?▼

Four formats are supported: CSV for spreadsheets and databases, JSON for APIs and NoSQL stores, SQL INSERT statements for relational databases, and executable Python scripts for custom or large-scale generation.

Can I enforce business rules on generated test data?▼

Yes, constraints like rating distributions, category-value relationships, and domain restrictions can be specified. For example, bug reports can be limited to low ratings and email fields restricted to realistic domains.

How many rows of dummy data can I generate?▼

The default is 100 rows, but you can specify any row count via the ROWS parameter. For very large datasets, the Python script output is recommended since it generates data programmatically on execution.

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

Use a Python script when you need regenerable, parameterized, or very large datasets, or when data must be recreated in different environments. Static CSV or SQL files are better for one-time imports and fixed test fixtures.

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