db-seeder

Seed PostgreSQL, MySQL, SQLite, and MongoDB databases with Faker-based fake data.

1|Updated Oct 31, 2025
One-click install
npx skills add https://github.com/AIA-11-HN-MIB/MIB-MockInterviewAIBot --skill db-seeder
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: db-seeder
Source: https://github.com/AIA-11-HN-MIB/MIB-MockInterviewAIBot/tree/main/.claude/skills/db-seeder
Command: npx skills add https://github.com/AIA-11-HN-MIB/MIB-MockInterviewAIBot --skill db-seeder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires faker, pyyaml, sqlalchemy, psycopg2-binary, pymysql, pymongo, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Manually populating databases with realistic test data for development, testing, or staging environments is time-consuming and often leads to inconsistent data. This skill automates the generation and seeding of diverse, fake data, ensuring consistent and production-like environments.

Core Features & Use Cases

  • Multi-Database Support: Seed PostgreSQL, MySQL, SQLite, and MongoDB using ORM-based patterns (SQLAlchemy, Django, Prisma).
  • Realistic Data Generation: Leverage the Faker library to create diverse and localized fake data, including Vietnamese-specific content.
  • Automated Schema Inspection: Automatically analyze your database schema to generate factory functions and JSON fixture templates.
  • Use Case: Quickly populate a local development database with 100 users and 500 posts, or generate a consistent set of test fixtures for your CI/CD pipeline, complete with foreign key relationships.

Quick Start

Ask the db-seeder skill to automatically detect my database configuration and suggest a command to seed it with 50 candidates.

Frequently Asked Questions about db-seeder

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

FAQPage Schema
How do I seed a PostgreSQL database with realistic test data?

Database seeding generates fake data automatically using Faker across 100+ field types. Configure your PostgreSQL connection, run the auto-detection script to analyze your schema, and execute the seeding command to populate tables with consistent, production-like test data for development or testing environments.

Can I use database seeding with MySQL, SQLite, and MongoDB?

Yes, database seeding supports PostgreSQL, MySQL, SQLite, and MongoDB. It works with ORM-based workflows including SQLAlchemy, Django, and Prisma, auto-detecting your database configuration and generating appropriate fixtures and factory functions for each platform.

How do I generate realistic test fixtures with foreign key relationships?

The seeding tool inspects your database schema automatically, generates factory functions that respect foreign key constraints, and creates JSON fixture templates. You specify the record count—like 100 users with 500 posts—and it generates related data maintaining referential integrity.

What's the fastest way to populate a local development database?

Database seeding automates population by detecting your schema, generating Faker-based data across field types, and executing seed scripts in one workflow. Ask the tool to detect your configuration and suggest a command—like seeding 50 candidates—without manual data entry.

Can I generate localized fake data for testing?

Yes, the Faker library supports diverse and localized fake data generation, including Vietnamese-specific content. This ensures your test data matches real-world scenarios for your target locale and region.