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siteseed

Discover web app schemas and seed realistic test data via APIs or UI.

1|Updated Mar 16, 2026
Search Tags:#automation#testing#API#UI#data#seeding#web
One-click install
npx skills add https://github.com/garricn/siteseed --skill siteseed
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: siteseed
Source: https://github.com/garricn/siteseed/tree/main
Command: npx skills add https://github.com/garricn/siteseed --skill siteseed

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @faker-js/faker, @modelcontextprotocol/sdk, js-yaml, zod, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of discovering an app's data model, generating realistic test data, and efficiently populating web applications for testing and development purposes.

Core Features & Use Cases

  • Data Model Discovery: Extracts app schemas from OpenAPI specifications or UI forms to understand data relationships.
  • Realistic Data Generation: Creates authentic-looking data tailored to each field's type and constraints.
  • Automated Seeding: Seeds data via API calls or form filling, enabling rapid environment setup for testing, demos, or development.
  • Use Case: Developers can initialize a staging environment with representative data, perform end-to-end testing, and iterate faster by mimicking real user data.

Quick Start

Use siteseed to discover the data model from your API, plan the seed size, and automatically populate your app with realistic data.

Frequently Asked Questions about siteseed

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

FAQPage Schema
How do I automate web app data seeding from an OpenAPI specification?▼

Automated data seeding from OpenAPI is achieved by extracting the app schema, generating realistic test data with Faker, and populating the application via API calls to streamline testing workflows.

What is the best way to generate realistic test data for API and UI testing?▼

Generating realistic test data involves discovering app schemas from OpenAPI or UI forms, then creating authentic data tailored to field types and constraints using Faker for automated seeding.

Does automated data seeding work with GraphQL and tRPC schemas?▼

Automated data seeding supports GraphQL and tRPC as data source methods, allowing you to discover data models and populate web applications through these specific API interactions.

How do I populate web application forms with realistic test data automatically?▼

Populating UI forms automatically is done by discovering data models from form interfaces, generating authentic field data, and using intelligent seeding strategies to fill the forms for testing.

What dependencies are required to seed test data into web applications?▼

Seeding test data requires Node.js, Zod for schema validation, Faker for data generation, js-yaml for parsing, and the Model Context Protocol SDK for integration.

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