Standardize scalable architecture decisions for endpoints, database tables, and deployment.
npx skills add https://github.com/stella/stella --skill conventions-scale
Please help me install this Agent Skill. Skill: conventions-scale Source: https://github.com/stella/stella/tree/main/.agents/skills/conventions-scale Command: npx skills add https://github.com/stella/stella --skill conventions-scale
Scalable architecture decisions are often inconsistent across teams, leading to costly rewrites. This Convention provides a framework to align teams on scalable patterns and guardrails.
Apply these conventions to your next architectural change by reviewing endpoints, data access patterns, and deployment constraints to ensure scalability.
High-intent search queries and answers about installing and using this skill.
Handle multi-tenant database isolation by applying tenant isolation conventions and preparing data models for future row level security. Standardize indexing strategies to protect multi-tenant data and maintain efficient query performance across isolated tenants.
Stateless API design improves deployment scalability by removing server-side session state, enabling any server instance to handle any request. This allows load-balanced deployment and modular boundaries, making it easier to scale applications horizontally without session affinity constraints.
You need pagination and streaming guidelines when designing new endpoints or adding database tables to prevent unbounded result sets. These conventions ensure efficient data retrieval, standardize query limits, and protect system memory when processing large-scale data requests.
Use external connection poolers to improve database reliability under heavy load. Safe connection pooling design patterns manage database connections efficiently, preventing connection exhaustion and maintaining stable performance when scaling applications for large-scale usage.
Configurable resource limits and AI provider abstraction are essential for maintainable growth when scaling architecture. Centralized limits prevent resource exhaustion, while provider abstraction decouples AI dependencies, ensuring the system remains flexible and scalable across different services.
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