Performance Optimizer

Make applications fast, scalable, and cost-efficient.

Core Principle

Measure first, optimize second. Don't guess at bottlenecks—profile, measure, then fix the slowest parts.

Performance Budget

Web Vitals (Target Metrics)

Core Web Vitals:
  Largest Contentful Paint (LCP): < 2.5s # Main content visible
  First Input Delay (FID): < 100ms # Interaction responsiveness
  Cumulative Layout Shift (CLS): < 0.1 # Visual stability

Additional Metrics:
  First Contentful Paint (FCP): < 1.8s # First content rendered
  Time to Interactive (TTI): < 3.8s # Fully interactive
  Total Blocking Time (TBT): < 200ms # Main thread blocked
  Speed Index: < 3.4s # Visual progress

Backend Metrics:
  API Response Time (P95): < 500ms
  Database Query Time (P95): < 100ms
  Server Response Time (TTFB): < 600ms

Phase 1: Profiling & Measurement

Goal: Identify actual bottlenecks, not perceived ones

Frontend Profiling

Chrome DevTools:

// 1. Performance tab → Record → Reload page
// 2. Analyze:
//    - Main thread activity
//    - Network waterfall
//    - JavaScript execution time
//    - Rendering time

// 3. Lighthouse audit
// Run: chrome://lighthouse or `npm i -g lighthouse`
lighthouse https://yoursite.com --view

React DevTools Profiler:

// Wrap component to profile
import { Profiler } from 'react'

function onRenderCallback(id, phase, actualDuration) {
  console.log(`${id} (${phase}) took ${actualDuration}ms`)
}

;<Profiler id="ExpensiveComponent" onRender={onRenderCallback}>
  <ExpensiveComponent />
</Profiler>

Backend Profiling

Node.js Profiling:

# Generate CPU profile
node --prof app.js

# Process profile
node --prof-process isolate-0x*.log > processed.txt

# Flame graphs (better visualization)
npm i -g 0x
0x app.js

Python Profiling:

import cProfile
import pstats

# Profile function
cProfile.run('slow_function()', 'output.prof')

# Analyze
p = pstats.Stats('output.prof')
p.sort_stats('cumulative').print_stats(20)

Database Profiling

PostgreSQL:

-- Enable query logging
ALTER DATABASE yourdb SET log_min_duration_statement = 100; -- Log queries >100ms

-- Analyze query
EXPLAIN (ANALYZE, BUFFERS)
SELECT * FROM users WHERE email = '[email protected]';

-- Find slow queries
SELECT query, mean_exec_time, calls
FROM pg_stat_statements
ORDER BY mean_exec_time DESC
LIMIT 20;

MongoDB:

// Enable profiling
db.setProfilingLevel(1, { slowms: 100 })

// View slow queries
db.system.profile.find({ millis: { $gt: 100 } }).sort({ ts: -1 })

// Explain query
db.collection.find({ email: '[email protected]' }).explain('executionStats')

Phase 2: Database Optimization

Add Strategic Indexes

-- Before: Table scan (slow)
SELECT * FROM users WHERE email = '[email protected]';
-- Execution time: 2000ms on 1M rows

-- After: Index scan (fast)
CREATE INDEX idx_users_email ON users(email);
SELECT * FROM users WHERE email = '[email protected]';
-- Execution time: 5ms

-- Composite index for multi-column queries
CREATE INDEX idx_posts_user_date ON posts(user_id, created_at DESC);
SELECT * FROM posts WHERE user_id = 123 ORDER BY created_at DESC;

-- Partial index for filtered queries
CREATE INDEX idx_active_users ON users(created_at) WHERE is_active = true;

Eliminate N+1 Queries

// ❌ Bad: N+1 query problem (101 database queries)
const users = await User.findAll() // 1 query
for (const user of users) {
  user.posts = await Post.findAll({ where: { userId: user.id } }) // N queries
}

// ✅ Good: Eager loading (2 queries)
const users = await User.findAll({
  include: [{ model: Post }]
})

// ✅ Better: DataLoader (batching + caching)
const userLoader = new DataLoader(async userIds => {
  const users = await User.findAll({ where: { id: userIds } })
  return userIds.map(id => users.find(u => u.id === id))
})

Query Optimization

-- Avoid SELECT *
-- ❌ Bad
SELECT * FROM users WHERE id = 1;

-- ✅ Good
SELECT id, name, email FROM users WHERE id = 1;

-- Use LIMIT
-- ❌ Bad
SELECT * FROM posts ORDER BY created_at DESC;

-- ✅ Good
SELECT * FROM posts ORDER BY created_at DESC LIMIT 20;
how to use performance-optimizer
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use performance-optimizer on Cursor
AI-first code editor with Composer
1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add performance-optimizer
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/daffy0208/ai-dev-standards --skill performance-optimizercopy
The skills CLI fetches performance-optimizer from GitHub repository daffy0208/ai-dev-standards and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/performance-optimizer
Reload or restart Cursor to activate performance-optimizer. Access the skill through slash commands (e.g., /performance-optimizer) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?