memstack-development-performance-audit

Use this skill when the user says 'performance audit', 'why is it slow', 'optimize performance', 'page speed', 'Core Web Vitals', 'lighthouse', 'load time', or needs to diagnose and fix frontend or backend performance issues. Do NOT use for code reviews or security audits.

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Repository: cwinvestments/memstack
Last source activity: April 1, 2026 at 13:01
Detected SKILL.md language: English
Stars: 417
Forks: 45

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name	memstack-development-performance-audit
description	Use this skill when the user says 'performance audit', 'why is it slow', 'optimize performance', 'page speed', 'Core Web Vitals', 'lighthouse', 'load time', or needs to diagnose and fix frontend or backend performance issues. Do NOT use for code reviews or security audits.
version	1.0.0
license	Proprietary — MemStack™ Pro by CW Affiliate Investments LLC. See LICENSE.txt

🚀 Performance Audit — Full-Stack Performance Scanner

Identify and prioritize performance bottlenecks across frontend, backend, and network layers with measured impact and fix priority.

Activation

When this skill activates, output:

🚀 Performance Audit — Scanning for performance bottlenecks...

Context	Status
User says "performance audit", "optimize", "slow app"	ACTIVE
User mentions bundle size, N+1 queries, or Core Web Vitals	ACTIVE
User wants to find memory leaks or unnecessary re-renders	ACTIVE
User wants database schema optimization specifically	DORMANT — see database-architect
User wants API design review (not performance)	DORMANT — see api-designer
User wants general code quality review	DORMANT — see code-reviewer

Protocol

Step 1: Gather Inputs

Ask the user for:

Stack: Frontend framework, backend language, database?
Symptoms: What feels slow? (initial load, interactions, API responses, builds)
Scale: How many users? How much data?
Metrics: Any existing performance data? (Lighthouse scores, APM dashboards)
Priority: User-facing speed or server-side efficiency?

Step 2: Frontend — Bundle Analysis

Check bundle size and composition:

# Next.js
npx @next/bundle-analyzer
# or: ANALYZE=true next build

# Webpack (generic)
npx webpack-bundle-analyzer stats.json

# Vite
npx vite-bundle-visualizer

What to look for:

Issue	Detection	Impact	Fix
Large dependencies	Bundle > 200KB gzipped	Slow initial load	Replace with lighter alternatives
Duplicate packages	Same lib in multiple versions	Wasted bytes	Dedupe or pin single version
Unused exports	Tree-shaking not working	Wasted bytes	Use ESM imports, avoid barrel files
No code splitting	Single large bundle	Slow initial load	Dynamic imports, route-based splitting
Unoptimized images	Images > 100KB without optimization	Slow load	next/image, sharp, WebP/AVIF
Missing compression	No gzip/brotli on responses	60-80% larger payloads	Enable in server/CDN config

Common heavy dependencies and alternatives:

Package	Size (minified)	Lighter Alternative	Size
`moment`	72 KB	`date-fns` (tree-shakeable)	~2-5 KB used
`lodash` (full)	72 KB	`lodash-es` (tree-shakeable)	~1-3 KB used
`axios`	14 KB	`fetch` (native)	0 KB
`classnames`	1 KB	Template literals	0 KB
`uuid`	3 KB	`crypto.randomUUID()`	0 KB
`chart.js`	65 KB	Specific chart lib for your use case	Varies

Tree-shaking checks:

── TREE-SHAKING ISSUES ────────────────────

[x] Barrel files (index.ts re-exporting everything)
    File: src/utils/index.ts
    Impact: Imports entire utils even if using one function
    Fix: Import directly from source file

[x] CommonJS modules (require() instead of import)
    File: src/legacy/helper.js
    Impact: Cannot tree-shake CJS modules
    Fix: Convert to ESM or find ESM version of dependency

[x] Side effects not declared
    Package: [name]
    Impact: Bundler can't safely remove unused code
    Fix: Add "sideEffects": false to package.json

Step 3: Frontend — Core Web Vitals

Audit for the three Core Web Vitals:

── CORE WEB VITALS AUDIT ──────────────────

LCP (Largest Contentful Paint) — Target: < 2.5s
  Current: [measured or estimated]
  Issues found:
    [ ] Hero image not optimized (missing width/height, no lazy loading)
    [ ] Render-blocking CSS in <head> (large stylesheet before content)
    [ ] Web fonts blocking render (no font-display: swap)
    [ ] Server response time > 600ms (TTFB too high)
    [ ] Third-party scripts blocking main thread

FID / INP (Interaction to Next Paint) — Target: < 200ms
  Current: [measured or estimated]
  Issues found:
    [ ] Long tasks on main thread (> 50ms blocks)
    [ ] Heavy JavaScript execution on page load
    [ ] Synchronous XHR calls blocking interaction
    [ ] Event handlers doing expensive computation

CLS (Cumulative Layout Shift) — Target: < 0.1
  Current: [measured or estimated]
  Issues found:
    [ ] Images without explicit dimensions (width/height)
    [ ] Dynamically injected content above fold
    [ ] Web fonts causing layout shift (FOUT/FOIT)
    [ ] Ads or embeds without reserved space

Quick LCP fixes:

// 1. Preload hero image
<link rel="preload" as="image" href="/hero.webp" fetchpriority="high" />

// 2. Font display swap
@font-face {
  font-family: 'CustomFont';
  src: url('/fonts/custom.woff2') format('woff2');
  font-display: swap;
}

// 3. Next.js Image component (automatic optimization)
import Image from 'next/image';
<Image src="/hero.jpg" width={1200} height={600} priority alt="Hero" />

Step 4: Frontend — React Performance

Unnecessary re-render detection:

Pattern	Detection	Impact	Fix
Passing new objects/arrays as props	`onClick={() => {}}` in JSX	Child re-renders every parent render	Extract handler, use `useCallback`
Missing memoization on expensive computation	Computed value in render body	Recalculated every render	`useMemo` for expensive calculations
Context causing tree-wide re-renders	Context value changes frequently	All consumers re-render	Split context, use selectors
Missing `key` on list items	React DevTools warnings	Incorrect DOM reconciliation	Add stable unique key
Props drilling deep component trees	Props passed through 5+ levels	Tight coupling, re-render chains	Context or composition pattern

React performance audit checklist:

── REACT PERFORMANCE ──────────────────────

Component rendering:
  [ ] React.memo on pure presentational components
  [ ] useCallback for handlers passed to memoized children
  [ ] useMemo for expensive computed values (> 1ms)
  [ ] Stable keys on list items (not array index for dynamic lists)

State management:
  [ ] State colocated to where it's used (not lifted too high)
  [ ] Context split: frequent-update values separate from static config
  [ ] Avoid storing derived state (compute from source of truth)
  [ ] Batch state updates where possible

Data fetching:
  [ ] Loading states prevent waterfall fetches
  [ ] Parallel fetches where data is independent
  [ ] Cache API responses (React Query, SWR)
  [ ] Pagination/infinite scroll for large lists

Rendering:
  [ ] Virtualize long lists (react-window, @tanstack/virtual)
  [ ] Lazy load below-fold components (React.lazy + Suspense)
  [ ] Avoid layout thrashing (reading DOM then writing immediately)
  [ ] Debounce/throttle scroll and resize handlers

Step 5: Backend — Database Performance

N+1 query detection:

── N+1 QUERY PATTERNS ─────────────────────

Pattern: Fetching related data in a loop
  Example:
    const users = await db.query('SELECT * FROM users');
    for (const user of users) {
      user.posts = await db.query('SELECT * FROM posts WHERE user_id = ?', [user.id]);
    }
  Queries generated: 1 + N (where N = number of users)

  Fix: JOIN or subquery
    SELECT u.*, p.* FROM users u
    LEFT JOIN posts p ON p.user_id = u.id;

  ORM fix (Prisma):
    await prisma.user.findMany({ include: { posts: true } });

  ORM fix (Drizzle):
    await db.query.users.findMany({ with: { posts: true } });

Missing index detection:

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

-- Find missing indexes
SELECT relname, seq_scan, seq_tup_read, idx_scan
FROM pg_stat_user_tables
WHERE seq_scan > 100 AND idx_scan < seq_scan / 10
ORDER BY seq_scan DESC;

-- Suggested indexes for common patterns:
-- Foreign keys: CREATE INDEX idx_posts_user_id ON posts(user_id);
-- Filtered queries: CREATE INDEX idx_users_active ON users(status) WHERE status = 'active';
-- Sorting: CREATE INDEX idx_posts_created ON posts(created_at DESC);
-- Text search: CREATE INDEX idx_users_email ON users(email);

Query optimization checklist:

Issue	Detection	Impact	Fix
N+1 queries	Loop with DB call inside	O(N) queries → O(1)	JOIN or eager loading
Missing indexes	Full table scans on filtered columns	Slow queries as data grows	Add targeted indexes
SELECT *	Fetching all columns	Excess data transfer	Select only needed columns
No pagination	Returning all records	Memory + transfer overhead	LIMIT/OFFSET or cursor pagination
Missing connection pooling	New connection per query	Connection overhead	Use pgBouncer or built-in pool
Unoptimized JOINs	Joining on non-indexed columns	Slow JOINs	Index JOIN columns

Step 6: Backend — API Performance

Over-fetching and under-fetching:

── API RESPONSE AUDIT ─────────────────────

Endpoint: GET /api/users
  Response size: 45 KB per request
  Fields returned: 28
  Fields used by client: 8
  Over-fetch ratio: 71% wasted

  Fix: Add field selection
    GET /api/users?fields=id,name,email,avatar
    or: implement GraphQL for flexible queries

Endpoint: GET /api/dashboard
  Requests made: 6 sequential API calls
  Could be: 1 aggregated endpoint
  Waterfall time: 1.8s
  Parallel time: 0.4s (if batched)

  Fix: Create /api/dashboard aggregate endpoint
    or: use Promise.all() on client for parallel fetches

Missing pagination:

── PAGINATION AUDIT ───────────────────────

Endpoint: GET /api/products
  Current: Returns ALL products (2,400 records)
  Response size: 890 KB
  Response time: 1.2s

  Fix: Add cursor-based pagination
    GET /api/products?cursor=abc123&limit=50
    Response: { data: [...], nextCursor: "def456", hasMore: true }

Caching opportunities:

── CACHE AUDIT ────────────────────────────

Endpoint             Cacheable?   TTL          Strategy
────────────────────────────────────────────────────────
GET /api/products    Yes          5 min        CDN + browser Cache-Control
GET /api/user/:id    Conditional  1 min        ETag / If-None-Match
GET /api/config      Yes          1 hour       CDN, stale-while-revalidate
POST /api/orders     No           —            Never cache mutations
GET /api/search      Maybe        30 sec       Vary by query params

Missing headers:
  [ ] Cache-Control not set on static assets
  [ ] No ETag on API responses
  [ ] No compression (gzip/brotli) on responses

Step 7: Backend — Memory & Resource Leaks

Common memory leak patterns:

Leak Type	Detection	Symptoms	Fix
Event listeners	Listeners added without removal	Memory grows over time	`removeEventListener` in cleanup
Unclosed DB connections	Connection pool exhaustion	"too many connections" errors	Close connections in finally/cleanup
Growing arrays/maps	In-memory cache without eviction	RSS grows indefinitely	Add TTL, LRU cache, or size limit
Unreferenced timers	`setInterval` without `clearInterval`	Timer continues after context dies	Clear timers on shutdown/cleanup
Unresolved promises	Promises that never settle	Memory for callbacks retained	Add timeouts to all async operations
Circular references	Objects referencing each other	GC can't collect (rare in V8)	Break cycles, use WeakRef

Detection approach:

# Node.js heap snapshot
node --inspect app.js
# Open Chrome DevTools → Memory → Take heap snapshot
# Compare two snapshots to find growing objects

# Watch RSS over time
watch -n 5 'ps -o rss -p $(pgrep -f "node app.js") | tail -1'

Cleanup patterns:

// Express: cleanup on server shutdown
const server = app.listen(3000);
const connections = new Set();
server.on('connection', (conn) => {
  connections.add(conn);
  conn.on('close', () => connections.delete(conn));
});

process.on('SIGTERM', () => {
  server.close();
  for (const conn of connections) conn.destroy();
  db.end();          // Close DB pool
  redis.quit();      // Close Redis
  clearInterval(heartbeat);  // Clear timers
});

Step 8: Network Performance

Unnecessary API calls:

── REDUNDANT REQUEST AUDIT ────────────────

Issue: Same data fetched multiple times
  /api/user/me called 4 times on dashboard load
  Fix: Cache response, share via context/store

Issue: Polling when WebSocket would work
  /api/notifications polled every 5 seconds
  Fix: WebSocket/SSE for real-time updates

Issue: No request deduplication
  Search input triggers API call per keystroke
  Fix: Debounce (300ms), cancel previous request (AbortController)

Issue: Large uncompressed responses
  /api/export returns 2.4 MB JSON without gzip
  Fix: Enable compression middleware

Compression check:

// Express
const compression = require('compression');
app.use(compression());

// Verify: response should have
// Content-Encoding: gzip  (or br for brotli)

CDN and static asset optimization:

── STATIC ASSET AUDIT ─────────────────────

Asset Type    Current          Optimized        Savings
────────────────────────────────────────────────────────
Images        PNG 2.4 MB       WebP 180 KB      92%
Fonts         4 font files     2 subsetted      60%
CSS           3 files, 120 KB  1 file, 45 KB    62%
JS            1.2 MB bundle    4 chunks, 280 KB  77%

Recommendations:
  [ ] Serve images in WebP/AVIF with fallback
  [ ] Subset fonts to used characters only
  [ ] Enable HTTP/2 for parallel asset loading
  [ ] Set Cache-Control: max-age=31536000 for hashed assets
  [ ] Use CDN for static assets (Cloudflare, CloudFront)

Step 9: Performance Scorecard

Generate a scored report:

━━━ PERFORMANCE SCORECARD ━━━━━━━━━━━━━━━━
Project: [name]
Audit date: [date]
Overall score: [X/100]

── FRONTEND ───────────────────────────────
Bundle size:        [X/25]  [size]KB gzipped
  Issues: [list]
Core Web Vitals:    [X/25]
  LCP: [value]s    [good/needs-work/poor]
  INP: [value]ms   [good/needs-work/poor]
  CLS: [value]     [good/needs-work/poor]
React performance:  [X/10]
  Re-render issues: [count]

── BACKEND ────────────────────────────────
Database:           [X/15]
  N+1 queries:      [count found]
  Missing indexes:  [count found]
API efficiency:     [X/10]
  Over-fetching:    [count endpoints]
  Missing cache:    [count endpoints]
  No pagination:    [count endpoints]
Memory safety:      [X/5]
  Potential leaks:  [count found]

── NETWORK ────────────────────────────────
Compression:        [X/5]   [enabled/missing]
Asset optimization: [X/5]   [optimized/unoptimized]

── PRIORITIZED FIX LIST ───────────────────

Priority  Issue                          Impact    Effort   Category
──────────────────────────────────────────────────────────────────────
🔴 P1     [issue description]            High      Low      [area]
🔴 P1     [issue description]            High      Medium   [area]
🟡 P2     [issue description]            Medium    Low      [area]
🟡 P2     [issue description]            Medium    Medium   [area]
🟠 P3     [issue description]            Low       Low      [area]
🟠 P3     [issue description]            Medium    High     [area]

Priority scoring:

P1 (Do now): High impact + Low-Medium effort — biggest wins
P2 (Plan soon): Medium impact or High effort for high impact
P3 (Backlog): Low impact or very high effort

Impact estimation guide:

Optimization	Typical Impact
Fix N+1 queries	50-90% reduction in query time
Add missing indexes	10-100x faster queries
Enable compression	60-80% smaller responses
Code splitting	30-60% faster initial load
Image optimization	50-90% smaller images
Remove unused deps	10-40% smaller bundle
Add caching	80-99% fewer API calls
Virtualize lists	90% less DOM, smooth scrolling

Inputs

Tech stack (frontend framework, backend language, database)
Performance symptoms (what feels slow)
Scale information (users, data volume)
Existing metrics (Lighthouse scores, APM data)
Priority area (user-facing or server-side)

Outputs

Bundle analysis with heavy dependency identification and alternatives
Core Web Vitals audit (LCP, INP, CLS) with specific fixes
React performance checklist (re-renders, memoization, data fetching)
Database audit (N+1 detection, missing indexes, query optimization)
API audit (over-fetching, pagination, caching opportunities)
Memory leak detection patterns and cleanup
Network audit (compression, CDN, redundant requests)
Performance scorecard (0-100) with prioritized fix list
Impact estimates per optimization

Level History

Lv.1 — Base: Full-stack performance scanner covering frontend (bundle analysis, Core Web Vitals, React re-renders), backend (N+1 queries, missing indexes, API over-fetching, memory leaks), and network (compression, CDN, caching). Performance scorecard with priority-ranked fix list and impact estimates. (Origin: MemStack v3.2, Mar 2026)

name	memstack-development-performance-audit
description	Use this skill when the user says 'performance audit', 'why is it slow', 'optimize performance', 'page speed', 'Core Web Vitals', 'lighthouse', 'load time', or needs to diagnose and fix frontend or backend performance issues. Do NOT use for code reviews or security audits.
version	1.0.0
license	Proprietary — MemStack™ Pro by CW Affiliate Investments LLC. See LICENSE.txt

🚀 Performance Audit — Full-Stack Performance Scanner

Identify and prioritize performance bottlenecks across frontend, backend, and network layers with measured impact and fix priority.

Activation

When this skill activates, output:

🚀 Performance Audit — Scanning for performance bottlenecks...

Context	Status
User says "performance audit", "optimize", "slow app"	ACTIVE
User mentions bundle size, N+1 queries, or Core Web Vitals	ACTIVE
User wants to find memory leaks or unnecessary re-renders	ACTIVE
User wants database schema optimization specifically	DORMANT — see database-architect
User wants API design review (not performance)	DORMANT — see api-designer
User wants general code quality review	DORMANT — see code-reviewer

Protocol

Step 1: Gather Inputs

Ask the user for:

Stack: Frontend framework, backend language, database?
Symptoms: What feels slow? (initial load, interactions, API responses, builds)
Scale: How many users? How much data?
Metrics: Any existing performance data? (Lighthouse scores, APM dashboards)
Priority: User-facing speed or server-side efficiency?

Step 2: Frontend — Bundle Analysis

Check bundle size and composition:

# Next.js
npx @next/bundle-analyzer
# or: ANALYZE=true next build

# Webpack (generic)
npx webpack-bundle-analyzer stats.json

# Vite
npx vite-bundle-visualizer

What to look for:

Issue	Detection	Impact	Fix
Large dependencies	Bundle > 200KB gzipped	Slow initial load	Replace with lighter alternatives
Duplicate packages	Same lib in multiple versions	Wasted bytes	Dedupe or pin single version
Unused exports	Tree-shaking not working	Wasted bytes	Use ESM imports, avoid barrel files
No code splitting	Single large bundle	Slow initial load	Dynamic imports, route-based splitting
Unoptimized images	Images > 100KB without optimization	Slow load	next/image, sharp, WebP/AVIF
Missing compression	No gzip/brotli on responses	60-80% larger payloads	Enable in server/CDN config

Common heavy dependencies and alternatives:

Package	Size (minified)	Lighter Alternative	Size
`moment`	72 KB	`date-fns` (tree-shakeable)	~2-5 KB used
`lodash` (full)	72 KB	`lodash-es` (tree-shakeable)	~1-3 KB used
`axios`	14 KB	`fetch` (native)	0 KB
`classnames`	1 KB	Template literals	0 KB
`uuid`	3 KB	`crypto.randomUUID()`	0 KB
`chart.js`	65 KB	Specific chart lib for your use case	Varies

Tree-shaking checks:

── TREE-SHAKING ISSUES ────────────────────

[x] Barrel files (index.ts re-exporting everything)
    File: src/utils/index.ts
    Impact: Imports entire utils even if using one function
    Fix: Import directly from source file

[x] CommonJS modules (require() instead of import)
    File: src/legacy/helper.js
    Impact: Cannot tree-shake CJS modules
    Fix: Convert to ESM or find ESM version of dependency

[x] Side effects not declared
    Package: [name]
    Impact: Bundler can't safely remove unused code
    Fix: Add "sideEffects": false to package.json

Step 3: Frontend — Core Web Vitals

Audit for the three Core Web Vitals:

── CORE WEB VITALS AUDIT ──────────────────

LCP (Largest Contentful Paint) — Target: < 2.5s
  Current: [measured or estimated]
  Issues found:
    [ ] Hero image not optimized (missing width/height, no lazy loading)
    [ ] Render-blocking CSS in <head> (large stylesheet before content)
    [ ] Web fonts blocking render (no font-display: swap)
    [ ] Server response time > 600ms (TTFB too high)
    [ ] Third-party scripts blocking main thread

FID / INP (Interaction to Next Paint) — Target: < 200ms
  Current: [measured or estimated]
  Issues found:
    [ ] Long tasks on main thread (> 50ms blocks)
    [ ] Heavy JavaScript execution on page load
    [ ] Synchronous XHR calls blocking interaction
    [ ] Event handlers doing expensive computation

CLS (Cumulative Layout Shift) — Target: < 0.1
  Current: [measured or estimated]
  Issues found:
    [ ] Images without explicit dimensions (width/height)
    [ ] Dynamically injected content above fold
    [ ] Web fonts causing layout shift (FOUT/FOIT)
    [ ] Ads or embeds without reserved space

Quick LCP fixes:

// 1. Preload hero image
<link rel="preload" as="image" href="/hero.webp" fetchpriority="high" />

// 2. Font display swap
@font-face {
  font-family: 'CustomFont';
  src: url('/fonts/custom.woff2') format('woff2');
  font-display: swap;
}

// 3. Next.js Image component (automatic optimization)
import Image from 'next/image';
<Image src="/hero.jpg" width={1200} height={600} priority alt="Hero" />

Step 4: Frontend — React Performance

Unnecessary re-render detection:

Pattern	Detection	Impact	Fix
Passing new objects/arrays as props	`onClick={() => {}}` in JSX	Child re-renders every parent render	Extract handler, use `useCallback`
Missing memoization on expensive computation	Computed value in render body	Recalculated every render	`useMemo` for expensive calculations
Context causing tree-wide re-renders	Context value changes frequently	All consumers re-render	Split context, use selectors
Missing `key` on list items	React DevTools warnings	Incorrect DOM reconciliation	Add stable unique key
Props drilling deep component trees	Props passed through 5+ levels	Tight coupling, re-render chains	Context or composition pattern

React performance audit checklist:

── REACT PERFORMANCE ──────────────────────

Component rendering:
  [ ] React.memo on pure presentational components
  [ ] useCallback for handlers passed to memoized children
  [ ] useMemo for expensive computed values (> 1ms)
  [ ] Stable keys on list items (not array index for dynamic lists)

State management:
  [ ] State colocated to where it's used (not lifted too high)
  [ ] Context split: frequent-update values separate from static config
  [ ] Avoid storing derived state (compute from source of truth)
  [ ] Batch state updates where possible

Data fetching:
  [ ] Loading states prevent waterfall fetches
  [ ] Parallel fetches where data is independent
  [ ] Cache API responses (React Query, SWR)
  [ ] Pagination/infinite scroll for large lists

Rendering:
  [ ] Virtualize long lists (react-window, @tanstack/virtual)
  [ ] Lazy load below-fold components (React.lazy + Suspense)
  [ ] Avoid layout thrashing (reading DOM then writing immediately)
  [ ] Debounce/throttle scroll and resize handlers

Step 5: Backend — Database Performance

N+1 query detection:

── N+1 QUERY PATTERNS ─────────────────────

Pattern: Fetching related data in a loop
  Example:
    const users = await db.query('SELECT * FROM users');
    for (const user of users) {
      user.posts = await db.query('SELECT * FROM posts WHERE user_id = ?', [user.id]);
    }
  Queries generated: 1 + N (where N = number of users)

  Fix: JOIN or subquery
    SELECT u.*, p.* FROM users u
    LEFT JOIN posts p ON p.user_id = u.id;

  ORM fix (Prisma):
    await prisma.user.findMany({ include: { posts: true } });

  ORM fix (Drizzle):
    await db.query.users.findMany({ with: { posts: true } });

Missing index detection:

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

-- Find missing indexes
SELECT relname, seq_scan, seq_tup_read, idx_scan
FROM pg_stat_user_tables
WHERE seq_scan > 100 AND idx_scan < seq_scan / 10
ORDER BY seq_scan DESC;

-- Suggested indexes for common patterns:
-- Foreign keys: CREATE INDEX idx_posts_user_id ON posts(user_id);
-- Filtered queries: CREATE INDEX idx_users_active ON users(status) WHERE status = 'active';
-- Sorting: CREATE INDEX idx_posts_created ON posts(created_at DESC);
-- Text search: CREATE INDEX idx_users_email ON users(email);

Query optimization checklist:

Issue	Detection	Impact	Fix
N+1 queries	Loop with DB call inside	O(N) queries → O(1)	JOIN or eager loading
Missing indexes	Full table scans on filtered columns	Slow queries as data grows	Add targeted indexes
SELECT *	Fetching all columns	Excess data transfer	Select only needed columns
No pagination	Returning all records	Memory + transfer overhead	LIMIT/OFFSET or cursor pagination
Missing connection pooling	New connection per query	Connection overhead	Use pgBouncer or built-in pool
Unoptimized JOINs	Joining on non-indexed columns	Slow JOINs	Index JOIN columns

Step 6: Backend — API Performance

Over-fetching and under-fetching:

── API RESPONSE AUDIT ─────────────────────

Endpoint: GET /api/users
  Response size: 45 KB per request
  Fields returned: 28
  Fields used by client: 8
  Over-fetch ratio: 71% wasted

  Fix: Add field selection
    GET /api/users?fields=id,name,email,avatar
    or: implement GraphQL for flexible queries

Endpoint: GET /api/dashboard
  Requests made: 6 sequential API calls
  Could be: 1 aggregated endpoint
  Waterfall time: 1.8s
  Parallel time: 0.4s (if batched)

  Fix: Create /api/dashboard aggregate endpoint
    or: use Promise.all() on client for parallel fetches

Missing pagination:

── PAGINATION AUDIT ───────────────────────

Endpoint: GET /api/products
  Current: Returns ALL products (2,400 records)
  Response size: 890 KB
  Response time: 1.2s

  Fix: Add cursor-based pagination
    GET /api/products?cursor=abc123&limit=50
    Response: { data: [...], nextCursor: "def456", hasMore: true }

Caching opportunities:

── CACHE AUDIT ────────────────────────────

Endpoint             Cacheable?   TTL          Strategy
────────────────────────────────────────────────────────
GET /api/products    Yes          5 min        CDN + browser Cache-Control
GET /api/user/:id    Conditional  1 min        ETag / If-None-Match
GET /api/config      Yes          1 hour       CDN, stale-while-revalidate
POST /api/orders     No           —            Never cache mutations
GET /api/search      Maybe        30 sec       Vary by query params

Missing headers:
  [ ] Cache-Control not set on static assets
  [ ] No ETag on API responses
  [ ] No compression (gzip/brotli) on responses

Step 7: Backend — Memory & Resource Leaks

Common memory leak patterns:

Leak Type	Detection	Symptoms	Fix
Event listeners	Listeners added without removal	Memory grows over time	`removeEventListener` in cleanup
Unclosed DB connections	Connection pool exhaustion	"too many connections" errors	Close connections in finally/cleanup
Growing arrays/maps	In-memory cache without eviction	RSS grows indefinitely	Add TTL, LRU cache, or size limit
Unreferenced timers	`setInterval` without `clearInterval`	Timer continues after context dies	Clear timers on shutdown/cleanup
Unresolved promises	Promises that never settle	Memory for callbacks retained	Add timeouts to all async operations
Circular references	Objects referencing each other	GC can't collect (rare in V8)	Break cycles, use WeakRef

Detection approach:

# Node.js heap snapshot
node --inspect app.js
# Open Chrome DevTools → Memory → Take heap snapshot
# Compare two snapshots to find growing objects

# Watch RSS over time
watch -n 5 'ps -o rss -p $(pgrep -f "node app.js") | tail -1'

Cleanup patterns:

// Express: cleanup on server shutdown
const server = app.listen(3000);
const connections = new Set();
server.on('connection', (conn) => {
  connections.add(conn);
  conn.on('close', () => connections.delete(conn));
});

process.on('SIGTERM', () => {
  server.close();
  for (const conn of connections) conn.destroy();
  db.end();          // Close DB pool
  redis.quit();      // Close Redis
  clearInterval(heartbeat);  // Clear timers
});

Step 8: Network Performance

Unnecessary API calls:

── REDUNDANT REQUEST AUDIT ────────────────

Issue: Same data fetched multiple times
  /api/user/me called 4 times on dashboard load
  Fix: Cache response, share via context/store

Issue: Polling when WebSocket would work
  /api/notifications polled every 5 seconds
  Fix: WebSocket/SSE for real-time updates

Issue: No request deduplication
  Search input triggers API call per keystroke
  Fix: Debounce (300ms), cancel previous request (AbortController)

Issue: Large uncompressed responses
  /api/export returns 2.4 MB JSON without gzip
  Fix: Enable compression middleware

Compression check:

// Express
const compression = require('compression');
app.use(compression());

// Verify: response should have
// Content-Encoding: gzip  (or br for brotli)

CDN and static asset optimization:

── STATIC ASSET AUDIT ─────────────────────

Asset Type    Current          Optimized        Savings
────────────────────────────────────────────────────────
Images        PNG 2.4 MB       WebP 180 KB      92%
Fonts         4 font files     2 subsetted      60%
CSS           3 files, 120 KB  1 file, 45 KB    62%
JS            1.2 MB bundle    4 chunks, 280 KB  77%

Recommendations:
  [ ] Serve images in WebP/AVIF with fallback
  [ ] Subset fonts to used characters only
  [ ] Enable HTTP/2 for parallel asset loading
  [ ] Set Cache-Control: max-age=31536000 for hashed assets
  [ ] Use CDN for static assets (Cloudflare, CloudFront)

Step 9: Performance Scorecard

Generate a scored report:

━━━ PERFORMANCE SCORECARD ━━━━━━━━━━━━━━━━
Project: [name]
Audit date: [date]
Overall score: [X/100]

── FRONTEND ───────────────────────────────
Bundle size:        [X/25]  [size]KB gzipped
  Issues: [list]
Core Web Vitals:    [X/25]
  LCP: [value]s    [good/needs-work/poor]
  INP: [value]ms   [good/needs-work/poor]
  CLS: [value]     [good/needs-work/poor]
React performance:  [X/10]
  Re-render issues: [count]

── BACKEND ────────────────────────────────
Database:           [X/15]
  N+1 queries:      [count found]
  Missing indexes:  [count found]
API efficiency:     [X/10]
  Over-fetching:    [count endpoints]
  Missing cache:    [count endpoints]
  No pagination:    [count endpoints]
Memory safety:      [X/5]
  Potential leaks:  [count found]

── NETWORK ────────────────────────────────
Compression:        [X/5]   [enabled/missing]
Asset optimization: [X/5]   [optimized/unoptimized]

── PRIORITIZED FIX LIST ───────────────────

Priority  Issue                          Impact    Effort   Category
──────────────────────────────────────────────────────────────────────
🔴 P1     [issue description]            High      Low      [area]
🔴 P1     [issue description]            High      Medium   [area]
🟡 P2     [issue description]            Medium    Low      [area]
🟡 P2     [issue description]            Medium    Medium   [area]
🟠 P3     [issue description]            Low       Low      [area]
🟠 P3     [issue description]            Medium    High     [area]

Priority scoring:

P1 (Do now): High impact + Low-Medium effort — biggest wins
P2 (Plan soon): Medium impact or High effort for high impact
P3 (Backlog): Low impact or very high effort

Impact estimation guide:

Optimization	Typical Impact
Fix N+1 queries	50-90% reduction in query time
Add missing indexes	10-100x faster queries
Enable compression	60-80% smaller responses
Code splitting	30-60% faster initial load
Image optimization	50-90% smaller images
Remove unused deps	10-40% smaller bundle
Add caching	80-99% fewer API calls
Virtualize lists	90% less DOM, smooth scrolling

Inputs

Tech stack (frontend framework, backend language, database)
Performance symptoms (what feels slow)
Scale information (users, data volume)
Existing metrics (Lighthouse scores, APM data)
Priority area (user-facing or server-side)

Outputs

Bundle analysis with heavy dependency identification and alternatives
Core Web Vitals audit (LCP, INP, CLS) with specific fixes
React performance checklist (re-renders, memoization, data fetching)
Database audit (N+1 detection, missing indexes, query optimization)
API audit (over-fetching, pagination, caching opportunities)
Memory leak detection patterns and cleanup
Network audit (compression, CDN, redundant requests)
Performance scorecard (0-100) with prioritized fix list
Impact estimates per optimization

Level History

Lv.1 — Base: Full-stack performance scanner covering frontend (bundle analysis, Core Web Vitals, React re-renders), backend (N+1 queries, missing indexes, API over-fetching, memory leaks), and network (compression, CDN, caching). Performance scorecard with priority-ranked fix list and impact estimates. (Origin: MemStack v3.2, Mar 2026)

memstack-development-performance-audit

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More from this repository

More from this repository

🚀 Performance Audit — Full-Stack Performance Scanner

Activation

Protocol

Step 1: Gather Inputs

Step 2: Frontend — Bundle Analysis

Step 3: Frontend — Core Web Vitals

Step 4: Frontend — React Performance

Step 5: Backend — Database Performance

Step 6: Backend — API Performance

Step 7: Backend — Memory & Resource Leaks

Step 8: Network Performance

Step 9: Performance Scorecard

Inputs

Outputs

Level History

🚀 Performance Audit — Full-Stack Performance Scanner

Activation

Protocol

Step 1: Gather Inputs

Step 2: Frontend — Bundle Analysis

Step 3: Frontend — Core Web Vitals

Step 4: Frontend — React Performance

Step 5: Backend — Database Performance

Step 6: Backend — API Performance

Step 7: Backend — Memory & Resource Leaks

Step 8: Network Performance

Step 9: Performance Scorecard

Inputs

Outputs

Level History