Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
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Slash command/dummy-dataset
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Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.
---name: dummy-dataset
description: "Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos."
---# Dummy Dataset Generation
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Creates executable scripts or direct data files for immediate use.
**Use when:** Creating test data, generating sample datasets, building realistic mock data for development, or populating test environments.
**Arguments:**
- `$PRODUCT`: The product or system name
- `$DATASET_TYPE`: Type of data (e.g., customer feedback, transactions, user profiles)
- `$ROWS`: Number of rows to generate (default: 100)
- `$COLUMNS`: Specific columns or fields to include
- `$FORMAT`: Output format (CSV, JSON, SQL, Python script)
- `$CONSTRAINTS`: Additional constraints or business rules
## Step-by-Step Process
1. **Identify dataset type** - Understand the data domain
2. **Define column specifications** - Names, data types, and value ranges
3. **Determine row count** - How many sample records needed
4. **Select output format** - CSV, JSON, SQL INSERT, or Python script
5. **Apply realistic patterns** - Ensure data looks authentic and valid
