andrecrjr/mockzilla

Overview

This skill generates high-fidelity, stateless mocks and dynamic JSON Schemas for Mockzilla. It focuses on realistic, production-like mock data using JSON Schema, Faker, and internal interpolation while avoiding any state changes or persistence.

How this skill works

I produce mocks primarily with create_schema_mock, combining JSON Schema with Faker syntax and {$.path} interpolation for internal consistency. Objects use additionalProperties: false and arrays use additionalItems: false, with minItems and maxItems set to control output size and keep results predictable.

When to use it

• Building UI prototypes that need realistic, varied data without backend changes
• Generating mock API responses for end-to-end tests and QA where no state should be modified
• Creating lists or paginated previews that require consistent internal references (e.g., emails derived from names)
• Producing sample responses for API docs or design reviews
• Replacing hardcoded examples with dynamic, realistic data for demos

Best practices

• Always use create_schema_mock for dynamic lists and realistic objects
• Set minItems and maxItems for arrays (global max recommended: 5) to keep responses manageable
• Enforce strict schemas: additionalProperties: false on objects and additionalItems: false on arrays
• Avoid hardcoding more than three fields; prefer Faker for realistic variability
• Use {$.path} interpolation to keep generated fields internally consistent
• Preview schemas with preview_mock before saving or publishing

Example use cases

• Generate a list of user profiles with consistent emails and avatars for a dashboard UI
• Produce an e-commerce product feed with realistic names, prices, and stock flags for integration tests
• Create financial transaction samples with randomized amounts and recent dates for analytics demos
• Mock API responses for QA scenarios where requests must not modify server state
• Provide realistic sample payloads for API documentation and developer onboarding