Agent skill

Dummy Dataset Generator

Generates realistic test datasets with custom columns, row counts and business constraints, output as CSV, JSON, SQL inserts or a runnable Python script.

MITAuto-check passedTesting & QA

Install Dummy Dataset Generator

skills CLI

$ npx skills add phuryn/pm-skills --skill dummy-dataset -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install phuryn/pm-skills dummy-dataset --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/pm-execution/skills/dummy-dataset .claude/skills/dummy-dataset && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "dummy-dataset" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-execution/skills/dummy-dataset into .claude/skills/dummy-dataset/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dummy-dataset", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/phuryn/pm-skills/tree/main/pm-execution/skills/dummy-dataset

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add phuryn/pm-skills --skill dummy-dataset -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install phuryn/pm-skills dummy-dataset --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/pm-execution/skills/dummy-dataset .agents/skills/dummy-dataset && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "dummy-dataset" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-execution/skills/dummy-dataset into .agents/skills/dummy-dataset/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dummy-dataset", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add phuryn/pm-skills --skill dummy-dataset -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install phuryn/pm-skills dummy-dataset --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/pm-execution/skills/dummy-dataset .cursor/skills/dummy-dataset && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "dummy-dataset" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-execution/skills/dummy-dataset into .cursor/skills/dummy-dataset/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dummy-dataset", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/phuryn/pm-skills.git --path pm-execution/skills/dummy-dataset

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add phuryn/pm-skills --skill dummy-dataset -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install phuryn/pm-skills dummy-dataset --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/pm-execution/skills/dummy-dataset .gemini/skills/dummy-dataset && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "dummy-dataset" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-execution/skills/dummy-dataset into .gemini/skills/dummy-dataset/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dummy-dataset", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install phuryn/pm-skills dummy-dataset

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add phuryn/pm-skills --skill dummy-dataset -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/pm-execution/skills/dummy-dataset .github/skills/dummy-dataset && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "dummy-dataset" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-execution/skills/dummy-dataset into .github/skills/dummy-dataset/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dummy-dataset", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add phuryn/pm-skills --skill dummy-dataset -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install phuryn/pm-skills dummy-dataset --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/phuryn/pm-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/pm-execution/skills/dummy-dataset .opencode/skills/dummy-dataset && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "dummy-dataset" agent skill from https://github.com/phuryn/pm-skills/tree/main/pm-execution/skills/dummy-dataset into .opencode/skills/dummy-dataset/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "dummy-dataset", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: dummy-dataset
GitHub stars: 27k
Token cost: ~983 tokens
SKILL.md length: 331 words
Files: 1
Skills in repo: 67
Repo updated: Sep 14, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Generates realistic test datasets with custom columns, row counts and business constraints, output as CSV, JSON, SQL inserts or a runnable Python script.

Works in 8 steps: Identify dataset type - Understand the… → Define column specifications - Names,… → Determine row count - How many sample… → …
Creating a mock dataset for a demo or development environment
SKILL.md covers Step-by-Step Process, Template: Python Script Output, Example Dataset Specification and Output Deliverables, plus 1 more section
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The agent works through a fixed process: identify the data domain, define column names, types and value ranges, decide the row count, pick an output format, apply realistic-looking patterns, apply any business constraints such as skewed rating distributions or category-dependent values, then generate the data or a script that generates it and validate the result. Parameters named in the skill are the product or system name, the dataset type, row count, which columns to include, the output format and any extra constraints, with a default of 100 rows when not specified.

A worked example builds a customer feedback dataset with columns like feedback_id, customer_name, email, feedback_date, rating, category and free-text feedback, using constraints such as a skewed star-rating distribution, bug reports only carrying low ratings, and realistic email domains. Deliverables include either a ready-to-execute Python script or a direct data file, plus documentation of how the data was generated and quick-start instructions for using it.

When your agent uses it

Creating a mock dataset for a demo or development environment
Generating sample rows with realistic constraints for a new database table
Producing test data in CSV, JSON or SQL insert format

Example prompts

“Generate 200 rows of dummy customer feedback data as CSV.”
“Create a mock transactions dataset with realistic amounts and give me SQL inserts.”
“Build a Python script that generates sample user profiles with valid emails.”

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

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

What it can do on your machine

Read from SKILL.md and the folder at commit 8607e3b. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dummy Dataset Generator loads about 983 tokens when it runs. Until then it costs about 64 tokens; SKILL.md has 331 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~64
When it runs · the whole SKILL.md, loaded when a task matches: ~983

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from phuryn/pm-skills at commit 8607e3b, republished under its MIT licence (© phuryn). 331 words, ~983 tokens.

Download SKILL.mdSave it as .claude/skills/dummy-dataset/SKILL.md (or your agent's skills folder).

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

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

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

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

Files

Just SKILL.md in pm-execution/skills/dummy-dataset of phuryn/pm-skills.

Open the folder on GitHubat commit 8607e3b

Compare with similar skills

Dummy Dataset Generator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dummy Dataset Generator compared with similar skills
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Dummy Datasetkillvxk/pm-skills-zh	167	—	~595	Automated safety check: Pass	MIT	6 mo ago
Update Megatron Golden ValuesNVIDIA/Megatron-LM	18k	—	~2.8k	Automated safety check: Pass	Custom licence	today
Forge Project Inityaomindong1996/forge-admin	125	—	~1.9k	Automated safety check: Notes	Apache-2.0	today

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Questions about Dummy Dataset Generator

What does Dummy Dataset Generator do?

Generates realistic test datasets with custom columns, row counts and business constraints, output as CSV, JSON, SQL inserts or a runnable Python script. The agent works through a fixed process: identify the data domain, define column names, types and value ranges, decide the row count, pick an output format, apply realistic-looking patterns, apply any business constraints such as skewed rating distributions or category-dependent values, then generate the data or a script that generates it and validate the result. Parameters named in the skill are the product or system name, the dataset type, row count, which columns to include, the output format and any extra constraints, with a default of 100 rows when not specified.

When should I use Dummy Dataset Generator?

Dummy Dataset Generator fits situations like: creating a mock dataset for a demo or development environment; generating sample rows with realistic constraints for a new database table; producing test data in CSV, JSON or SQL insert format.

How do I install Dummy Dataset Generator in Claude Code?

Run `npx skills add phuryn/pm-skills --skill dummy-dataset -a claude-code`. Or copy the skill folder (pm-execution/skills/dummy-dataset in phuryn/pm-skills) into .claude/skills/dummy-dataset in your project. Claude Code loads it when a task matches its description.

How do I install Dummy Dataset Generator in Codex?

Run `npx skills add phuryn/pm-skills --skill dummy-dataset -a codex`. Or copy the skill folder (pm-execution/skills/dummy-dataset in phuryn/pm-skills) into .agents/skills/dummy-dataset in your project. Codex loads it when a task matches its description.

Can I use Dummy Dataset Generator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add phuryn/pm-skills --skill dummy-dataset -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dummy-dataset, .gemini/skills/dummy-dataset, .github/skills/dummy-dataset and .opencode/skills/dummy-dataset in your project.

What does Dummy Dataset Generator need to run?

SKILL.md names no scripts, command-line tools or credentials: Dummy Dataset Generator is instructions for the agent only.

Does Dummy Dataset Generator access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Dummy Dataset Generator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Dummy Dataset Generator use?

Dummy Dataset Generator is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dummy Dataset Generator use?

About 983 tokens (SKILL.md is roughly 3.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Dummy Dataset Generator?

Skills that share tags, products or a category with Dummy Dataset Generator: Phy Test Data Factory (LeoYeAI/openclaw-master-skills, 2.2k stars), Django Filter Benchmark (saleor/saleor, 23k stars), Dummy Dataset (killvxk/pm-skills-zh, 167 stars) and Update Megatron Golden Values (NVIDIA/Megatron-LM, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dummy Dataset Generator?

phuryn (a GitHub user) maintains it in phuryn/pm-skills, which has 26,845 GitHub stars. The repository holds 67 skills in this directory. The repository was last updated on September 14, 2026.

Source: phuryn/pm-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Dummy Dataset Generator

Install Dummy Dataset Generator

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Dummy Dataset Generation

Step-by-Step Process

Template: Python Script Output

Example Dataset Specification

Output Deliverables

Output Formats

Files

Compare with similar skills

Similar skills

Phy Test Data Factory

Django Filter Benchmark

Dummy Dataset

Update Megatron Golden Values

Forge Project Init

Agent-Core Python Testing

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Questions about Dummy Dataset Generator

Install Dummy Dataset Generator

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Dummy Dataset Generation

Step-by-Step Process

Template: Python Script Output

Example Dataset Specification

Output Deliverables

Output Formats

Files

Compare with similar skills

Similar skills

More from phuryn/pm-skills

Related

Questions about Dummy Dataset Generator