How do I generate realistic test data with this skill?

Use the included `generate_seed.py` script, which utilizes the Faker library to create schema-aware mock data for your tables automatically.

Do I need to manually install Python dependencies?

No, the helper scripts are designed to run with 'uv', meaning dependencies are managed automatically without manual installation.

Can this skill handle very large datasets?

Yes, the skill provides patterns for the PostgreSQL COPY command for bulk loading and supports DVC integration to manage seed files larger than 1MB.

Is the seeding process idempotent?

Yes, the toolkit provides templates that use ON CONFLICT DO NOTHING clauses to ensure seed files can be run multiple times without creating duplicate records.

How does the skill maintain the order of execution?

It uses a numbered prefix convention (e.g., 01_lookups.sql, 02_users.sql) to ensure scripts run in the correct order to respect foreign key constraints.

Supabase Seeding Toolkit

Name: Supabase Seeding Toolkit
Author: ninyawee

byninyawee

数据库管理

Populates Supabase databases with high-quality seed data, lookup tables, and large datasets using specialized scripts and best practices.

关于

The Supabase Seeding toolkit provides a robust framework for managing database population throughout the development lifecycle. It streamlines the creation of lookup tables, generates realistic test data using Faker, and handles massive datasets via PostgreSQL COPY commands and DVC (Data Version Control) integration. By enforcing best practices like idempotency and ordered execution, it ensures consistent environments across local development and production-like staging setups, making it an essential tool for developers working within the Supabase and PostgreSQL ecosystem.

主要功能

Progress-monitored execution of multiple SQL seed files in numbered order
Bulk data loading optimizations using the COPY command for large datasets
Pre-configured templates for idempotent INSERT statements and transactions
Automated seed generation using Faker for realistic test environments
DVC integration to track large seed files without bloating Git repositories
0 GitHub stars

使用场景

Managing large-scale datasets across distributed development teams using DVC
Generating thousands of mock records for performance testing and UI development
Initializing lookup and reference tables during project setup

关于

主要功能

Progress-monitored execution of multiple SQL seed files in numbered order
Bulk data loading optimizations using the COPY command for large datasets
Pre-configured templates for idempotent INSERT statements and transactions
Automated seed generation using Faker for realistic test environments
DVC integration to track large seed files without bloating Git repositories
0 GitHub stars

使用场景

Managing large-scale datasets across distributed development teams using DVC
Generating thousands of mock records for performance testing and UI development
Initializing lookup and reference tables during project setup