Does Scalable Thinking provide specific code patterns?

Yes, it includes concrete examples for TypeScript interfaces, RESTful endpoint structures, SQL schemas, and cursor-based pagination patterns to replace non-scalable alternatives.

How does this skill prevent premature optimization?

It provides a 'When to Scale Up' guide that recommends adding complexity only when specific triggers are met, such as database queries exceeding 2 seconds or team sizes growing beyond 5 developers.

When should I use the Scalable Thinking skill in my workflow?

It is best used during Phase 2 (Design) and Phase 5 (Implementation) of development, specifically when working on architecture, API design, data modeling, or database schemas.

What is the core philosophy of the Scalable Thinking skill?

The core philosophy is 'Think Scalable, Build Simple.' It focuses on designing data models and architectures that can grow (Scalable) while ensuring the current implementation remains easy to understand and maintain (KISS).

Scalable Thinking

Name: Scalable Thinking
Author: nguyenthienthanh

bynguyenthienthanh

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API Development

Guides software architecture design to balance long-term scalability with the Keep It Simple, Stupid (KISS) principle.

About

Scalable Thinking is a specialized Claude Code skill that helps developers build systems capable of 10x growth without falling into the trap of premature optimization. It provides domain-specific patterns for data modeling, API versioning, and database schema design, ensuring that implementations remain simple and maintainable while being architecturally sound. By following the 'Think Scalable, Build Simple' philosophy, this skill assists in creating feature-based file structures, implementing cursor-based pagination, and establishing structured error handling, making it an essential tool for early-to-mid stage project development.

Key Features

1 GitHub stars
Architectural guidance for balancing scalability with implementation simplicity
Best practices for normalized and indexed database schema design
Actionable checklists for identifying when to add complexity based on metrics
Feature-based file structure recommendations for growing codebases
Standardized patterns for RESTful API versioning and cursor-based pagination

Use Cases

Refactoring a monolithic codebase into a scalable, feature-based directory structure
Designing a new database schema that needs to support high-growth data volume
Developing a versioned API contract that avoids common non-scalable anti-patterns

About

Key Features

1 GitHub stars
Architectural guidance for balancing scalability with implementation simplicity
Best practices for normalized and indexed database schema design
Actionable checklists for identifying when to add complexity based on metrics
Feature-based file structure recommendations for growing codebases
Standardized patterns for RESTful API versioning and cursor-based pagination

Use Cases

Refactoring a monolithic codebase into a scalable, feature-based directory structure
Designing a new database schema that needs to support high-growth data volume
Developing a versioned API contract that avoids common non-scalable anti-patterns