Skip to content
TrustedSkillsbeta
Back to all skills

Test Data Factory Builder

Listed
monkey1sai·vlatest·testing

Helps with testing, data as part of agent workflows workflows.

Repository

Install on your platform

This skill no longer installs under the name test-data-factory-builder. monkey1sai/openai-cli is deleted or private, so it cannot be installed from.

We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-10-22. We don't show a command we know will fail.

Repository install instructions

What we know about this skill

Publisher

monkey1sai

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. repository not found or private Source recorded as monkey1sai/openai-cli.
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The test-data-factory-builder skill generates structured test data based on user-defined schemas. It allows you to create synthetic datasets for testing applications, validating models, or populating databases. This skill simplifies the process of creating realistic and varied test data without relying on existing production data.

When to use it

  • Software Testing: Generate sample customer records for testing an e-commerce application's checkout flow.
  • Model Validation: Create a dataset of synthetic images with labels to evaluate the performance of an image recognition model.
  • Database Seeding: Populate a new database with realistic data for development or demonstration purposes.
  • Data Anonymization Testing: Generate test datasets that mimic anonymized production data, allowing you to validate your anonymization processes.

Key capabilities

  • Schema definition: Define the structure and fields of the desired dataset.
  • Data generation: Create synthetic data based on the provided schema.
  • Output formatting: Export generated data in various formats (likely JSON or CSV - not explicitly stated).

Example prompts

  • "Create 10 customer records with names, emails, and addresses."
  • "Generate a dataset of 5 product listings including name, description, price, and category."
  • “Build test data for an order processing system. The schema includes order ID, customer ID, item list, shipping address.”

Tips & gotchas

The quality of the generated data depends heavily on the accuracy and completeness of the provided schema definition. Ensure your schema accurately reflects the desired data structure and constraints to avoid unexpected or unusable results.