smithery.ai

performance-optimization

Full-stack performance analysis, optimization patterns, and monitoring strategies

First seen Mar 21, 2026

Installation

$ npx skills add https://smithery.ai

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Skill metadata

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Version1.0.0
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Package contents

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  • skill md SKILL.md 5,003 B
  • docs SUMMARY.md 113 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 19 installs

SKILL.md

Performance Optimization Skill

Comprehensive frameworks for analyzing and optimizing application performance across the entire stack.

When to Use

  • Application feels slow or unresponsive
  • Database queries taking too long
  • Frontend bundle size too large
  • API response times exceed targets
  • Core Web Vitals need improvement
  • Preparing for scale or high traffic

Performance Targets

Core Web Vitals (Frontend)

Metric Good Needs Work
LCP (Largest Contentful Paint) < 2.5s < 4s
INP (Interaction to Next Paint) < 200ms < 500ms
CLS (Cumulative Layout Shift) < 0.1 < 0.25
TTFB (Time to First Byte) < 200ms < 600ms

Backend Targets

Operation Target
Simple reads < 100ms
Complex queries < 500ms
Write operations < 200ms
Index lookups < 10ms

Bottleneck Categories

Category Symptoms Tools
Network High TTFB, slow loading Network tab, WebPageTest
Database Slow queries, pool exhaustion EXPLAIN ANALYZE, pgstatstatements
CPU High usage, slow compute Profiler, flame graphs
Memory Leaks, GC pauses Heap snapshots
Rendering Layout thrashing React DevTools, Performance tab

Database Optimization

Key Patterns

  1. Add Missing Indexes - Turn Seq Scan into Index Scan
  2. Fix N+1 Queries - Use JOINs or include instead of loops
  3. Cursor Pagination - Never load all records
  4. Connection Pooling - Manage connection lifecycle

Quick Diagnostics

-- Find slow queries (PostgreSQL)
SELECT query, calls, mean_time / 1000 as mean_seconds
FROM pg_stat_statements ORDER BY total_time DESC LIMIT 10;

-- Verify index usage
EXPLAIN ANALYZE SELECT * FROM orders WHERE user_id = 123;

See templates/database-optimization.ts for N+1 fixes and pagination patterns

Caching Strategy

Cache Hierarchy

L1: In-Memory (LRU, memoization) - fastest
L2: Distributed (Redis/Memcached) - shared
L3: CDN (edge, static assets) - global
L4: Database (materialized views) - fallback

Cache-Aside Pattern

const cached = await redis.get(key);
if (cached) return JSON.parse(cached);
const data = await db.query(...);
await redis.setex(key, 3600, JSON.stringify(data));
return data;

See templates/caching-patterns.ts for full implementation

Frontend Optimization

Bundle Optimization

  1. Code Splitting - lazy() for route-based splitting
  2. Tree Shaking - Import only what you need
  3. Image Optimization - WebP/AVIF, lazy loading, proper sizing

Rendering Optimization

  1. Memoization - memo(), useCallback(), useMemo()
  2. Virtualization - Render only visible items in long lists
  3. Batch DOM Operations - Read all, then write all

See templates/frontend-optimization.tsx for patterns

Analysis Commands

# Lighthouse audit
lighthouse http://localhost:3000 --output=json

# Bundle analysis
npx @next/bundle-analyzer  # Next.js
npx vite-bundle-visualizer # Vite

API Optimization

Response Optimization

  1. Field Selection - Return only requested fields
  2. Compression - Enable gzip/brotli (threshold: 1KB)
  3. ETags - Enable 304 responses for unchanged data
  4. Pagination - Cursor-based for large datasets

See templates/api-optimization.ts for middleware examples

Monitoring Checklist

Before Launch

  • Lighthouse score > 90
  • Core Web Vitals pass
  • Bundle size within budget
  • Database queries profiled
  • Compression enabled
  • CDN configured

Ongoing

  • Performance monitoring active
  • Alerting for degradation
  • Lighthouse CI in pipeline
  • Weekly query analysis
  • Real User Monitoring (RUM)

See templates/performance-metrics.ts for Prometheus metrics setup

Extended Thinking Triggers

Use Opus 4.5 extended thinking for:

  • Complex debugging - Multiple potential causes
  • Architecture decisions - Caching strategy selection
  • Trade-off analysis - Memory vs CPU vs latency
  • Root cause analysis - Performance regression investigation

Templates Reference

Template Purpose
database-optimization.ts N+1 fixes, pagination, pooling
caching-patterns.ts Redis cache-aside, memoization
frontend-optimization.tsx React memo, virtualization, code splitting
api-optimization.ts Compression, ETags, field selection
performance-metrics.ts Prometheus metrics, performance budget