Does it support DynamoDB single-table design?

Yes, it features specific patterns for DynamoDB single-table design, including PK/SK composite key strategies and Global Secondary Index (GSI) usage.

Can this skill help with database performance optimization?

Yes, it includes advanced strategies for indexing, aggregation pipeline optimization, and performance scripts to validate index coverage.

Which document databases are supported by this skill?

This skill provides comprehensive implementation guides and patterns for MongoDB, AWS DynamoDB, and Google Cloud Firestore.

When should I use this skill instead of a standard SQL skill?

Use this skill when your application requires flexible schemas, hierarchical JSON-like data, horizontal scaling, or rapid development without complex ORM migrations.

Does it provide code examples for specific languages?

It includes production-ready examples for Python (FastAPI), TypeScript (Next.js), Rust, and Go, covering various SDKs and drivers.

Document Database Implementation

Name: Document Database Implementation
Author: ancoleman

byancoleman

•

158

数据库管理

Implements flexible-schema NoSQL database patterns including MongoDB, DynamoDB, and Firestore with optimized indexing and aggregation strategies.

关于

This skill provides expert guidance for architecting and implementing document-based NoSQL databases, specifically optimized for AI-assisted development with Claude. It covers the full lifecycle of database management, from choosing between MongoDB, DynamoDB, or Firestore to implementing complex schema design patterns like embedding versus referencing. Developers gain access to production-grade indexing strategies, aggregation pipeline patterns, and multi-language code examples in Python, TypeScript, Rust, and Go, ensuring high-performance data layers for modern applications.

主要功能

Comprehensive support for MongoDB, AWS DynamoDB, and Google Cloud Firestore
Decision frameworks for schema design, including embedding vs. referencing
Multi-language implementation examples for Python, TS, Rust, and Go
Pre-built aggregation pipeline patterns and query optimization scripts
Advanced indexing strategies for compound, partial, and TTL indexes
158 GitHub stars

使用场景

Building scalable content management systems and user profile stores
Implementing high-throughput event logging and real-time data catalogs
Architecting serverless backends with optimized NoSQL data modeling

关于

主要功能

Comprehensive support for MongoDB, AWS DynamoDB, and Google Cloud Firestore
Decision frameworks for schema design, including embedding vs. referencing
Multi-language implementation examples for Python, TS, Rust, and Go
Pre-built aggregation pipeline patterns and query optimization scripts
Advanced indexing strategies for compound, partial, and TTL indexes
158 GitHub stars

使用场景

Building scalable content management systems and user profile stores
Implementing high-throughput event logging and real-time data catalogs
Architecting serverless backends with optimized NoSQL data modeling