# Dummy Dataset

Generate realistic dummy datasets for testing with customizable columns, constraints...

- Repository: https://github.com/phuryn/pm-skills (27597 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-10-29
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add phuryn/pm-skills --skill dummy-dataset -g -a claude-code -y`
Codex: `npx skills add phuryn/pm-skills --skill dummy-dataset -g -a codex -y`
Cursor: `npx skills add phuryn/pm-skills --skill dummy-dataset -g -a cursor -y`
Gemini CLI: `npx skills add phuryn/pm-skills --skill dummy-dataset -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add phuryn/pm-skills`

## 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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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
6. **Add business constraints** - Respect business logic and relationships
7. **Generate or script data** - Create executable output
8. **Validate output** - Ensure data quality and completeness

## Template: Python Script Output

```python
import csv
import json
from datetime import datetime, timedelta
import random

# Configuration
ROWS = $ROWS
FILENAME = "$DATASET_TYPE.csv"

# Column definitions with realistic value generators
columns = {
    "id": "auto-increment",
    "name": "first_last_name",
    "email": "email",
    "created_at": "timestamp",
    # Add more columns...
}

def generate_dataset():
    """Generate realistic dummy dataset"""
    data = []
    for i in range(1, ROWS + 1):
        record = {
            "id": f"U{i:06d}",
            # Generate values based on column definitions
        }
        data.append(record)
    return data

The rest: https://github.com/phuryn/pm-skills/blob/HEAD/pm-execution/skills/dummy-dataset/SKILL.md