mock-gen

What This Skill Does

The mock-gen skill is a powerful utility within OpenClaw designed to eliminate the tedious manual effort of creating test data. Instead of spending hours crafting JSON fixtures or manually populating database tables, developers can provide a natural language prompt, a TypeScript interface, or a JSON schema, and the skill will generate realistic, domain-specific mock data instantly. It supports multiple output formats, including JSON, CSV, and SQL inserts, making it versatile enough to support frontend prototypes, database seeding, or backend API testing. It abstracts away the complexity of mock data generation, allowing developers to focus on building features rather than populating environments.

Installation

To integrate this tool into your OpenClaw environment, use the following command: clawhub install openclaw/skills/skills/lxgicstudios/mock-gen

Ensure you have Node.js version 18 or higher installed on your system. No complex configuration is required—the tool is designed to work immediately upon installation, utilizing the power of AI to interpret your intent without needing tedious setup files.

Use Cases

• Database Seeding: Populate your development database with hundreds of consistent, realistic records to test performance and edge cases.
• Frontend Prototyping: Generate realistic API responses for UI development before the actual backend services are ready.
• Integration Testing: Create complex datasets that mimic real-world scenarios to ensure your data processing pipelines function correctly.
• Product Demos: Build visually appealing, data-rich dashboards that look professional during live presentations.

Example Prompts

1. "Generate 20 user records for a fintech app, including UUID, masked credit card numbers, transaction amounts, and timestamps in JSON format."
2. "Create a CSV dataset of 50 products for an inventory system, with columns for SKU, item name, price, stock quantity, and manufacturer category."
3. "Using the schema in ./types/User.ts, generate 100 sample user profiles and output them as a SQL file for database seeding."

Tips & Limitations

To get the most out of mock-gen, specificity is your greatest asset. Rather than requesting "generic users," describe the constraints, such as "users with addresses specifically in the UK" or "orders with random statuses including pending, shipped, and returned." While the tool is highly effective at mimicking patterns, always verify the output in critical production-adjacent testing environments. For complex schema structures, providing an explicit TypeScript interface or JSON schema file is significantly more reliable than relying solely on descriptive text. If you are generating massive datasets, consider breaking them into smaller, iterative batches to maintain high output quality and control.