Can this skill help me decide between MongoDB and PostgreSQL?

Yes, it includes a detailed selection guide that compares schema flexibility, consistency requirements, and scaling needs to help you choose the right database for your project.

Can I use this for production database administration?

Yes, it covers critical production tasks such as backup/restore strategies, user permission management, replication setup, and performance monitoring.

Does it support advanced query features like aggregation pipelines?

Absolutely. The skill provides implementation patterns for complex MongoDB aggregation stages as well as PostgreSQL window functions, CTEs, and advanced joins.

Are there automated scripts for database maintenance?

The skill references Python utilities for generating migrations, performing backups, and analyzing slow queries to recommend performance improvements.

Database Management & Optimization

Name: Database Management & Optimization
Author: Microck

byMicrock

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데이터베이스 관리

Streamlines database architecture, query development, and performance tuning for MongoDB and PostgreSQL environments.

소개

This skill serves as a comprehensive guide for managing both document-oriented (MongoDB) and relational (PostgreSQL) databases within Claude. It assists developers in selecting the ideal database architecture for their specific use case, writing complex queries or aggregation pipelines, and optimizing index performance. Beyond basic CRUD operations, the skill provides deep technical guidance on production-level tasks including schema migrations, replication, sharding strategies, and disaster recovery, ensuring your data layer is both scalable and resilient.

주요 기능

Performance optimization tools for index analysis and query execution plans
Dual support for MongoDB (BSON/NoSQL) and PostgreSQL (SQL/Relational) workflows
Automated schema design and data modeling best practices
81 GitHub stars
Production administration guides for backups, migrations, and user security
Advanced query generation for SQL, CTEs, and MongoDB Aggregation Pipelines

사용 사례

Refactoring slow-running database queries and optimizing index coverage
Managing complex database migrations and multi-region replication setups
Designing a scalable data model for high-traffic web applications

소개

주요 기능

Performance optimization tools for index analysis and query execution plans
Dual support for MongoDB (BSON/NoSQL) and PostgreSQL (SQL/Relational) workflows
Automated schema design and data modeling best practices
81 GitHub stars
Production administration guides for backups, migrations, and user security
Advanced query generation for SQL, CTEs, and MongoDB Aggregation Pipelines

사용 사례

Refactoring slow-running database queries and optimizing index coverage
Managing complex database migrations and multi-region replication setups
Designing a scalable data model for high-traffic web applications