Can it help reduce frontend bundle sizes?

Absolutely. It suggests techniques like tree-shaking, lazy loading routes, and specific library imports to decrease initial load times.

Does this skill work for both frontend and backend?

Yes, it provides a full-stack optimization framework covering database queries, API architecture, and frontend rendering strategies.

How does it help with database performance?

It includes patterns for eager loading, composite indexing, projection, and database-level paging to minimize data transfer and query time.

Which frameworks are supported?

While the optimization logic is language-agnostic, the skill provides specific implementation patterns for .NET (C#), SQL Server, and Angular.

Architecture Performance Optimizer

Name: Architecture Performance Optimizer
Author: CongDon1207

byCongDon1207

•

データベース管理

Analyzes and resolves performance bottlenecks across database queries, API endpoints, and frontend rendering frameworks.

概要

The Architecture Performance Optimizer skill empowers Claude to systematically diagnose and fix performance issues throughout the software stack. By following a structured workflow—from gathering baseline metrics to implementing advanced patterns like eager loading, parallel API operations, and virtual scrolling—this skill ensures applications are scalable and responsive. It is an essential tool for developers looking to eliminate common anti-patterns like N+1 queries, unbounded result sets, and synchronous I/O in high-traffic production environments.

主な機能

Advanced database optimization techniques including N+1 detection and indexing
Frontend efficiency patterns like lazy loading and OnPush change detection
Background job optimization with bounded parallelism and batch processing
Structured performance analysis framework for systematic bottleneck identification
API throughput enhancement via parallel processing and DTO projection
1 GitHub stars

ユースケース

Reducing API latency and database load times in enterprise-grade applications
Refactoring backend code to replace inefficient loops with batched database operations
Optimizing large-scale frontend applications for faster initial load and smoother UI

概要

主な機能

Advanced database optimization techniques including N+1 detection and indexing
Frontend efficiency patterns like lazy loading and OnPush change detection
Background job optimization with bounded parallelism and batch processing
Structured performance analysis framework for systematic bottleneck identification
API throughput enhancement via parallel processing and DTO projection
1 GitHub stars

ユースケース

Reducing API latency and database load times in enterprise-grade applications
Refactoring backend code to replace inefficient loops with batched database operations
Optimizing large-scale frontend applications for faster initial load and smoother UI