performance

Profiles and optimizes application performance across backend, frontend, database, and build layers.

Updated Jun 2, 2026
One-click install
npx skills add https://github.com/medispis/opencode-config --skill performance-medispis
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: performance
Source: https://github.com/medispis/opencode-config/tree/main/skills/performance
Command: npx skills add https://github.com/medispis/opencode-config --skill performance-medispis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Slow applications frustrate users and waste infrastructure costs, but guessing at bottlenecks often leads to wasted effort. This Skill provides a structured workflow to profile real hotspots, identify root causes, and apply measured optimizations across every layer of your stack. ## Core Features & Use Cases - Multi-layer profiling: Analyze backend response times, frontend Core Web Vitals (TTFB, FCP, LCP, CLS), database slow queries and EXPLAIN plans, and build times. - Targeted optimization strategies: Apply proven techniques like caching, query indexing, code splitting, lazy loading, connection pooling, and response compression. - Measured results: Enforces before/after measurement with a structured report showing baseline metrics, bottlenecks found, and optimizations applied. - Use Case: Your API endpoint takes 2.3 seconds to respond. Use this Skill to profile the request path, discover a missing database index via EXPLAIN analysis, add the index, and verify the query time drops to 45ms. ## Quick Start Analyze why my application's dashboard page loads slowly and optimize the biggest bottlenecks with before and after measurements.

Frequently Asked Questions about performance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find performance bottlenecks in my application?

Profile the application first rather than guessing. Measure backend response times and CPU hot paths, frontend metrics like LCP and bundle size, database slow query logs with EXPLAIN plans, and build step durations to locate actual hotspots.

How to optimize slow database queries?

Start with the slow query log and run EXPLAIN on complex queries to check index usage. Common fixes include adding missing indexes, batching queries, paginating results, using materialized views for aggregations, and partitioning large tables.

What frontend metrics should I measure for performance?

Track Time to First Byte (TTFB), First Contentful Paint (FCP), Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS), JavaScript execution time, and bundle size using tools like webpack-bundle-analyzer.

When should I not optimize code for performance?

Skip optimization when profiling shows no real bottleneck, when the gain is under 5 percent, or when it sacrifices correctness. Avoid adding complexity for marginal improvements and always optimize the biggest impact first.

How do I reduce frontend bundle size?

Analyze the bundle with webpack-bundle-analyzer, then apply code splitting and lazy loading, optimize images with WebP and responsive sizes, preload critical resources, and remove unused dependencies.