Why does this skill focus on measurement first?

Optimization without data is guesswork. By measuring first, you ensure that time is spent fixing actual bottlenecks rather than performing 'micro-optimizations' that have no real impact on user experience.

Does this skill work with any programming language?

While it includes specific examples for TypeScript, Elixir, and SQL, the core principles of profiling, measuring, and targeting bottlenecks are applicable to any software stack.

Can this skill help reduce my JavaScript bundle size?

Yes, it provides specific strategies for tree shaking, code splitting, and using native alternatives to heavy libraries to significantly decrease the size of your distribution files.

How does it handle database performance issues?

The skill guides users through interpreting execution plans (EXPLAIN ANALYZE), adding appropriate indexes, and refactoring queries to avoid common issues like N+1 fetches.

What is the 'Cardinal Rule' of this skill?

The Cardinal Rule is to never optimize without measuring first. This prevents premature optimization and ensures that the codebase remains maintainable while becoming faster.

Performance Optimizer

Name: Performance Optimizer
Author: TheBushidoCollective

byTheBushidoCollective

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Analíticas y Monitorización

Optimizes software performance using a systematic, measurement-driven approach to identify and eliminate bottlenecks across the full stack.

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The Performance Optimization skill empowers Claude to transform slow applications into high-performance systems through a rigorous 'measure, don't guess' methodology. It provides structured guidance for establishing performance baselines, profiling code to find the root cause of latency, and applying targeted fixes across frontend, backend, and database layers. Whether you are battling large JavaScript bundles, slow SQL queries, or inefficient algorithms, this skill ensures every change is data-backed, verified for correctness, and documented with clear impact metrics.

Características Principales

Data-driven validation process to compare 'Before' and 'After' metrics.
Targeted optimization patterns including lazy loading, memoization, and N+1 query resolution.
39 GitHub stars
Comprehensive checklists for bundle size reduction, memory management, and caching strategies.
Algorithm complexity analysis to replace inefficient logic with optimized data structures.
Systematic profiling for frontend (Core Web Vitals), backend (latency), and databases (query plans).

Casos de Uso

Reducing backend response times by implementing efficient caching and batch processing.
Diagnosing and fixing slow page loads or high Time to Interactive (TTI) in web applications.
Optimizing database performance by identifying missing indexes and refactoring complex joins.

Acerca de

Características Principales

Data-driven validation process to compare 'Before' and 'After' metrics.
Targeted optimization patterns including lazy loading, memoization, and N+1 query resolution.
39 GitHub stars
Comprehensive checklists for bundle size reduction, memory management, and caching strategies.
Algorithm complexity analysis to replace inefficient logic with optimized data structures.
Systematic profiling for frontend (Core Web Vitals), backend (latency), and databases (query plans).

Casos de Uso

Reducing backend response times by implementing efficient caching and batch processing.
Diagnosing and fixing slow page loads or high Time to Interactive (TTI) in web applications.
Optimizing database performance by identifying missing indexes and refactoring complex joins.