vague

Generate realistic test data conforming to Vague schemas with constraints and cross-references.

1|Updated Dec 18, 2025

Search Tags:#constraints #generation #test-data #data-generation #mock-data #superposition

Ready to run

npx skills add https://github.com/mcclowes/omg --skill vague

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: vague
Source: https://github.com/mcclowes/omg/tree/main/.claude/skills/vague
Command: npx skills add https://github.com/mcclowes/omg --skill vague

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the creation of realistic, structured test data with constraints, references, and superposition logic built-in.

Core Features & Use Cases

Test Data Generation: Create datasets with realistic values, unique constraints, and cross-references effortlessly.

Core Features & Use Cases

Data Generation: Create realistic test data with superposition, ranges, and computed fields.

Quick Start

Generate 50 realistic user profiles with unique emails, realistic names, and age ranges between 18 and 65.

Frequently Asked Questions about vague

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate realistic test data with constraints and cross-references?▼

Generate test data using Vague schemas that enforce constraints, unique values, and relationships between fields. The Skill creates realistic datasets like user profiles, invoices, and orders with deterministic seeding for reproducibility in testing environments.

Can I generate test data with superposition and computed fields?▼

Yes. Vague supports superposition for multiple valid values, ranges for numeric fields, computed fields for derived data, and plugin integrations with Faker and Regex. This enables complex, multi-variant test datasets from a single schema definition.

How do I eliminate manual mock creation for API testing?▼

Define your API spec or data schema once, then generate realistic mock datasets automatically. The Skill handles realistic names, emails, age ranges, and cross-references, reducing manual mock authoring and ensuring test data consistency.

What's the best way to generate datasets with deterministic seeding?▼

Use Vague's deterministic seeding to generate the same test data across runs, enabling reproducible testing. CLI tooling supports inference from schemas, validation, and multiple output formats for dataset generation.

Does this work for generating large-scale test datasets with relationships?▼

Yes. Generate datasets at scale—like 50+ user profiles—while maintaining referential integrity, unique constraints, and field relationships. Vague handles private fields, refinements, and Issuer plugins for specialized value generation.