dummy-dataset
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.
- Installs
- 2.7K
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/phuryn/pm-skills --skill dummy-dataset- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads dummy-dataset automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
- Identify dataset type - Understand the data domain
- Define column specifications - Names, data types, and value ranges
- Determine row count - How many sample records needed
- Select output format - CSV, JSON, SQL INSERT, or Python script
- Apply realistic patterns - Ensure data looks authentic and valid
- Add business constraints - Respect business logic and relationships
- Generate or script data - Create executable output
- Validate output - Ensure data quality and completeness
Template: Python Script Output
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
def save_as_csv(data, filename):
"""Save dataset as CSV"""
with open(filename, 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=data[0].keys())
writer.writeheader()
writer.writerows(data)
if __name__ == "__main__":
dataset = generate_dataset()
save_as_csv(dataset, FILENAME)
print(f"Generated {len(dataset)} records in {FILENAME}")
Example Dataset Specification
Dataset Type: Customer Feedback
Columns:
- feedback_id (auto-increment, U001, U002...)
- customer_name (realistic names)
- email (valid email format)
- feedback_date (dates last 90 days)
- rating (1-5 stars)
- category (Bug, Feature Request, Complaint, Praise)
- text (realistic feedback)
- product (electronics, clothing, home)
Constraints:
- Ratings skewed: 40% 5-star, 30% 4-star, 20% 3-star, 10% 1-2 star
- Bug category only with ratings 1-3
- Feature requests only with ratings 3-5
- Email domains realistic (gmail, yahoo, company.com)
Output Deliverables
- Ready-to-execute Python script OR direct data file
- CSV file with proper headers and formatting
- JSON file with valid structure and types
- SQL INSERT statements for database population
- Data validation and constraint compliance
- Realistic, business-appropriate values
- Documentation of data generation logic
- Quick-start instructions for using the dataset
Output Formats
CSV: Flat tabular format, easy to import into spreadsheets and databases
JSON: Nested structure, ideal for APIs and NoSQL databases
SQL: INSERT statements, directly executable on relational databases
Python Script: Executable generator for custom or large datasets
FAQ
What does the dummy-dataset skill do?
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.
How do I install dummy-dataset?
Run npx skills add https://github.com/phuryn/pm-skills --skill dummy-dataset in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/dummy-dataset/ (personal) or .claude/skills/dummy-dataset/ (project).
Which AI agents can use dummy-dataset?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is dummy-dataset safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains dummy-dataset?
It is published by phuryn in phuryn/pm-skills. It has 2,732 recorded installs.
More from phuryn/pm-skills
create-prd
phuryn/pm-skills
Create a lean, decision-ready Product Requirements Document with an 8-section template: summary, contacts, background, objective, market segments, value propositions (6-part JTBD template), solution, and release. Lean by default (about one to two pages), with a fuller spec on request. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.
pre-mortem
phuryn/pm-skills
Run a pre-mortem risk analysis on a PRD or launch plan. Categorizes risks as Tigers (real problems), Paper Tigers (overblown concerns), and Elephants (unspoken worries), then classifies as launch-blocking, fast-follow, or track. Use when preparing for launch, stress-testing a product plan, or identifying what could go wrong.
prioritization-frameworks
phuryn/pm-skills
Reference guide to 9 prioritization frameworks with formulas, when-to-use guidance, and templates — RICE, ICE, Kano, MoSCoW, Opportunity Score, and more. Use when selecting a prioritization method, comparing frameworks like RICE vs ICE, or learning how different prioritization approaches work.
outcome-roadmap
phuryn/pm-skills
Transform an output-focused roadmap into an outcome-focused one that communicates strategic intent. Rewrites initiatives as outcome statements reflecting user and business impacts. Use when shifting to outcome roadmaps, making a roadmap more strategic, or rewriting feature lists as outcomes.
strategy-red-team
phuryn/pm-skills
Red-team a PRD, roadmap, or strategy by attacking its load-bearing assumptions before reality does. Steelmans then attacks each claim, ranks failure modes by impact × likelihood × cheapness-to-test, and returns the cheapest test and kill criteria for each. Use when stress-testing a plan, pressure-testing a strategy, challenging assumptions, or preparing a doc for executive review.
competitor-analysis
phuryn/pm-skills
Analyze competitors with strengths, weaknesses, and differentiation opportunities. Identifies direct competitors and maps the competitive landscape. Use when doing competitive research, preparing a competitive brief, or finding differentiation opportunities.
Popular data & databases skills
supabase-postgres-best-practices
supabase/agent-skills
Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pgcron, pgmq), vector/semantic search (pgvector), and restoring dumps (pgrestore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.
prisma-client-api
prisma/skills
Prisma Client API reference covering model queries, filters, operators, and client methods. Use when writing database queries, using CRUD operations, filtering data, or configuring Prisma Client. Triggers on "prisma query", "findMany", "create", "update", "delete", "$transaction".
prisma-database-setup
prisma/skills
Deprecated compatibility name for prisma-orm-setup. Use only when a user or existing integration explicitly invokes prisma-database-setup; otherwise select prisma-orm-setup.
prisma-cli
prisma/skills
Prisma ORM 7 CLI reference for existing Prisma 7 applications or explicitly selected Prisma 7 workflows. Covers prisma init, generate, migrate, db, dev, studio, and mcp. For setup, connection repair, or Prisma 8, use prisma-orm-setup; not the Prisma Platform CLI.
prisma-postgres
prisma/skills
Deprecated compatibility name for prisma-postgres-setup. Use only when a user or existing integration explicitly invokes prisma-postgres; otherwise select prisma-postgres-setup.
prisma-driver-adapter-implementation
prisma/skills
Required reference for Prisma ORM 7 SQL driver adapter work. Use when implementing or modifying adapters, adding database drivers, or touching SqlDriverAdapter, Transaction, savepoint, result mapping, or DriverAdapterError behavior. Covers current transaction lifecycle, optional savepoint hooks, original database-error preservation, and verification.