Cache Strategy Implementation

I'll analyze your application and implement appropriate caching strategies to improve performance and reduce server load.

Arguments: $ARGUMENTS - cache type focus (e.g., "http", "service-worker", "redis", "browser")

Strategic Planning Process

1. Application Analysis

   • What type of application? (SPA, MPA, API, static site)
   • What data changes frequently vs. rarely?
   • What's cached currently, if anything?
   • Client-side, server-side, or both?
   • CDN usage and configuration

2. Cache Layer Selection

   • Browser cache (HTTP headers)
   • Service worker cache (offline-first PWA)
   • Application cache (in-memory, localStorage)
   • Server cache (Redis, Memcached)
   • CDN cache (edge caching)
   • Database query cache

3. Cache Invalidation Strategy

   • Time-based expiration (TTL)
   • Event-based invalidation
   • Version-based cache busting
   • Manual invalidation mechanisms
   • Stale-while-revalidate patterns

4. Performance vs. Freshness Tradeoff

   • Critical real-time data (no cache or very short TTL)
   • Semi-dynamic data (short TTL, stale-while-revalidate)
   • Static assets (long TTL, immutable)
   • User-specific data (private cache) </think>

Phase 1: Cache Audit

MANDATORY FIRST STEPS:

1. Detect application type and architecture
2. Analyze current caching configuration
3. Identify cacheable resources
4. Determine cache invalidation needs

Let me analyze your current caching setup:

# Check for existing cache configurations
echo "=== Cache Configuration Audit ==="

# Check for service worker
if [ -f "public/service-worker.js" ] || [ -f "src/service-worker.js" ] || [ -f "sw.js" ]; then
    echo "✓ Service Worker detected"
    ls -lh **/service-worker.js **/sw.js 2>/dev/null | head -5
else
    echo "✗ No Service Worker found"
fi

# Check for HTTP caching headers (common web server configs)
if [ -f ".htaccess" ]; then
    echo "✓ Apache .htaccess found"
    grep -i "cache-control\|expires" .htaccess 2>/dev/null | head -5
fi

if [ -f "nginx.conf" ] || [ -f "nginx/*.conf" ]; then
    echo "✓ Nginx config found"
    grep -i "cache\|expires" nginx*.conf 2>/dev/null | head -5
fi

# Check for Redis/Memcached dependencies
if grep -q "\"redis\"" package.json 2>/dev/null; then
    echo "✓ Redis client installed"
fi

if grep -q "\"memcached\"" package.json 2>/dev/null; then
    echo "✓ Memcached client installed"
fi

# Check for caching libraries
if grep -q "\"workbox\"" package.json 2>/dev/null; then
    echo "✓ Workbox (service worker toolkit) installed"
fi

# Check CDN configuration
if [ -f "vercel.json" ] || [ -f "netlify.toml" ]; then
    echo "✓ CDN configuration detected"
fi

Phase 2: Cache Strategy Design

Based on application type, I'll design appropriate caching layers:

Browser Cache Strategy (HTTP Headers)

Static Assets:

• Long cache duration (1 year)
• Immutable for versioned assets
• Public caching allowed
• Proper ETag configuration

Dynamic Content:

• Short cache duration or no-cache
• Private cache for user-specific data
• Stale-while-revalidate for better UX
• Proper cache-control directives

API Responses:

• Cache-Control based on data freshness
• ETag for conditional requests
• Vary headers for content negotiation
• Private cache for authenticated requests

Service Worker Cache Strategy

Cache-First (Offline-First):

• Static assets, fonts, images
• Application shell
• Third-party libraries

Network-First:

• API calls
• Dynamic content
• Real-time data

Stale-While-Revalidate:

• Semi-dynamic content
• News feeds, product listings
• Balance freshness with performance

Cache-Only:

• Fallback offline pages
• Critical UI assets

Application-Level Caching

In-Memory Caching:

• Computed values (memoization)
• Expensive calculations
• API response caching
• Query result caching

Local Storage:

• User preferences
• Authentication tokens
• Offline data sync
• Application state persistence

Server-Side Caching

Redis/Memcached:

• Database query results
• Computed data
• Session storage
• API response caching
• Rate limiting data

CDN Edge Caching:

• Static assets
• API responses (when appropriate)
• Geographic distribution
• DDoS protection

Phase 3: Implementation

I'll implement selected caching strategies:

HTTP Caching Headers

For Node.js/Express:

// Static assets with long-term caching
app.use('/static', express.static('public', {
  maxAge: '1y',
  immutable: true,
  etag: true
}));

// API responses with short-term caching
app.use('/api', (req, res, next) => {
  res.set('Cache-Control', 'private, max-age=300'); // 5 minutes
  next();
});

For Next.js:

// next.config.js
module.exports = {
  async headers() {
    return [
      {
        source: '/_next/static/:path*',
        headers: [
          {
            key: 'Cache-Control',
            value: 'public, max-age=31536000, immutable',
          },
        ],
      },
    ];
  },
};

For Nginx:

# Static assets
location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2)$ {
    expires 1y;
    add_header Cache-Control "public, immutable";
}

# HTML files - no cache
location ~* \.html$ {
    expires -1;
    add_header Cache-Control "no-cache, no-store, must-revalidate";
}

Service Worker Implementation

Workbox Configuration:

import { precacheAndRoute } from 'workbox-precaching';
import { registerRoute } from 'workbox-routing';
import { CacheFirst, NetworkFirst, StaleWhileRevalidate } from 'workbox-strategies';
import { ExpirationPlugin } from 'workbox-expiration';
import { CacheableResponsePlugin } from 'workbox-cacheable-response';

// Precache static assets
precacheAndRoute(self.__WB_MANIFEST);

// Cache images with Cache First strategy
registerRoute(
  ({ request }) => request.destination === 'image',
  new CacheFirst({
    cacheName: 'images',
    plugins: [
      new ExpirationPlugin({
        maxEntries: 60,
        maxAgeSeconds: 30 * 24 * 60 * 60, // 30 Days
      }),
    ],
  })
);

// API calls with Network First strategy
registerRoute(
  ({ url }) => url.pathname.startsWith('/api/'),
  new NetworkFirst({
    cacheName: 'api-cache',
    plugins: [
      new CacheableResponsePlugin({
        statuses: [0, 200],
      }),
      new ExpirationPlugin({
        maxAgeSeconds: 5 * 60, // 5 minutes
      }),
    ],
  })
);

// CSS and JS with Stale While Revalidate
registerRoute(
  ({ request }) => request.destination === 'style' || request.destination === 'script',
  new StaleWhileRevalidate({
    cacheName: 'static-resources',
  })
);

Memoization Patterns

React Memoization:

import { useMemo, useCallback } from 'react';
import { memo } from 'react';

// Memoize expensive calculations
const ExpensiveComponent = ({ data }) => {
  const processedData = useMemo(() => {
    return expensiveCalculation(data);
  }, [data]);

  const handleClick = useCallback(() => {
    // Handler logic
  }, []);

  return <div>{processedData}</div>;
};

export default memo(ExpensiveComponent);

Function Memoization:

// Simple memoization utility
function memoize(fn) {
  const cache = new Map();
  return (...args) => {
    const key = JSON.stringify(args);
    if (cache.has(key)) {
      return cache.get(key);
    }
    const result = fn(...args);
    cache.set(key, result);
    return result;
  };
}

// LRU cache with size limit
class LRUCache {
  constructor(limit = 100) {
    this.cache = new Map();
    this.limit = limit;
  }

  get(key) {
    if (!this.cache.has(key)) return undefined;
    const value = this.cache.get(key);
    this.cache.delete(key);
    this.cache.set(key, value); // Move to end
    return value;
  }

  set(key, value) {
    if (this.cache.has(key)) {
      this.cache.delete(key);
    } else if (this.cache.size >= this.limit) {
      const firstKey = this.cache.keys().next().value;
      this.cache.delete(firstKey);
    }
    this.cache.set(key, value);
  }
}

Redis Caching

Express with Redis:

const redis = require('redis');
const client = redis.createClient();

// Cache middleware
const cache = (duration) => {
  return async (req, res, next) => {
    const key = `cache:${req.originalUrl}`;

    try {
      const cached = await client.get(key);
      if (cached) {
        return res.json(JSON.parse(cached));
      }

      // Store original send function
      const originalSend = res.json.bind(res);

      // Override send to cache response
      res.json = (body) => {
        client.setex(key, duration, JSON.stringify(body));
        return originalSend(body);
      };

      next();
    } catch (err) {
      next();
    }
  };
};

// Use cache middleware
app.get('/api/data', cache(300), async (req, res) => {
  const data = await fetchData();
  res.json(data);
});

Phase 4: Cache Invalidation

I'll implement appropriate invalidation strategies:

Time-Based Expiration:

• Set appropriate TTL values
• Use max-age headers
• Configure Redis expiration
• Implement cleanup routines

Event-Based Invalidation:

• Clear cache on data updates
• Invalidate related cache entries
• Use cache tags for grouped invalidation
• Implement webhook-based clearing

Version-Based Cache Busting:

• Content hashing for static assets
• API versioning
• Service worker updates
• Cache key versioning

Token Optimization

Status: ✅ Fully Optimized (Phase 2 Batch 4B, 2026-01-27)

Baseline: 3,000-5,000 tokens → Optimized: 1,000-1,800 tokens (60-75% reduction)

This skill is meta - it implements caching FOR applications while using caching optimization strategies itself!

Core Optimization Strategies

1. Template-Based Cache Patterns (75% savings)

Cache: .claude/cache/cache-strategy/cache_patterns.json

{
  "redis": {
    "express": "// Express Redis middleware...",
    "nextjs": "// Next.js Redis client...",
    "django": "# Django Redis cache backend..."
  },
  "service_worker": {
    "workbox": "// Workbox config...",
    "vanilla": "// Vanilla SW..."
  },
  "http_headers": {
    "nginx": "location ~* ...",
    "apache": "ExpiresActive On...",
    "express": "app.use(express.static..."
  },
  "in_memory": {
    "react": "useMemo hook...",
    "node": "LRU cache class...",
    "python": "functools.lru_cache..."
  }
}

Strategy:

• Read 1 pattern template (200 tokens) vs. searching examples (2,000+ tokens)
• Framework-specific snippets instantly available
• No pattern re-generation across sessions

2. Focus Area Identification (70% savings)

Grep before Read approach:

# Identify cache targets (100 tokens vs. 2,000 reading all code)
grep -r "fetch\|axios\|api\." src/ --files-with-matches
grep -r "SELECT\|query\|find\(" src/ --files-with-matches
grep -r "expensive\|calculation\|compute" src/ --files-with-matches

Cache: .claude/cache/cache-strategy/hot_paths.json

{
  "api_routes": ["src/api/users.js", "src/api/products.js"],
  "db_queries": ["src/models/User.js"],
  "expensive_calculations": ["src/utils/analytics.js"],
  "last_scan": "2026-01-27T10:00:00Z"
}

Strategy:

• Only read files with cache opportunities
• Skip non-relevant code (tests, configs, types)
• Focus on high-impact areas first

3. Cached Best Practices (80% savings)

Cache: .claude/cache/cache-strategy/recommendations.json

{
  "ttl_guidelines": {
    "static_assets": "1y (31536000s)",
    "api_responses": "5m (300s)",
    "user_data": "no-cache",
    "computed_data": "1h (3600s)"
  },
  "invalidation_patterns": {
    "time_based": "Use max-age + stale-while-revalidate",
    "event_based": "Cache tags + manual clear",
    "version_based": "Content hashing for assets"
  },
  "cache_strategies": {
    "cache_first": "Static assets, images, fonts",
    "network_first": "API calls, real-time data",
    "stale_while_revalidate": "Semi-dynamic content"
  }
}

Strategy:

• Instant recommendations (no re-thinking)
• Consistent TTL values across projects
• Proven invalidation patterns

4. Framework-Specific Templates (70% savings)

Cache: .claude/cache/cache-strategy/framework_templates.json

{
  "express_redis": {
    "middleware": "const cache = (duration) => {...}",
    "client_setup": "const redis = require('redis')...",
    "error_handling": "try { cached = await client.get... }"
  },
  "nextjs_swr": {
    "config": "export const fetcher = ...",
    "revalidation": "revalidateOnFocus: false..."
  },
  "django_redis": {
    "settings": "CACHES = { 'default': {...} }",
    "decorator": "@cache_page(60 * 15)..."
  }
}

Strategy:

• Framework detected once (package.json/requirements.txt)
• Load only relevant templates
• No generic examples - specific to tech stack

5. Progressive Implementation (60% savings)

Cache: .claude/cache/cache-strategy/implementation_status.json

{
  "src/api/users.js": {
    "status": "redis_cache_added",
    "ttl": 300,
    "last_modified": "2026-01-27T10:00:00Z"
  },
  "src/components/ExpensiveChart.js": {
    "status": "memoization_added",
    "technique": "useMemo",
    "last_modified": "2026-01-27T09:00:00Z"
  },
  "public/service-worker.js": {
    "status": "workbox_configured",
    "strategies": ["CacheFirst", "NetworkFirst"],
    "last_modified": "2026-01-26T15:00:00Z"
  }
}

Strategy:

• Track what's already cached
• Skip implemented areas
• Focus on remaining high-impact targets

Optimization Workflow

Initial Cache Audit (300 tokens vs. 2,000)

# Fast detection (5 commands vs. reading all configs)
ls -la public/service-worker.js 2>/dev/null
grep -q "redis\|memcached" package.json
ls -la nginx.conf .htaccess 2>/dev/null
grep -q "workbox\|sw-precache" package.json
ls -la vercel.json netlify.toml 2>/dev/null

Focus Area Selection (200 tokens vs. 1,500)

Arguments-based routing:

• http → HTTP headers only (400 tokens)
• redis → Redis caching only (500 tokens)
• service-worker → PWA caching only (600 tokens)
• No args → Full analysis (1,800 tokens)

Template Application (400 tokens vs. 2,000)

# Load cached template, insert into file
# No generation, no examples, direct application

Token Budget Allocation

Total Budget: 1,000-1,800 tokens

1. Cache Audit (300 tokens)

   • Framework detection: 50 tokens
   • Existing cache scan: 100 tokens
   • Focus area identification: 150 tokens

2. Strategy Selection (200 tokens)

   • Load cached recommendations: 100 tokens
   • Match to detected framework: 100 tokens

3. Template Loading (300 tokens)

   • Load framework template: 150 tokens
   • Load best practices: 150 tokens

4. Implementation (400-800 tokens)

   • Apply templates: 200-400 tokens
   • Targeted file edits: 200-400 tokens

5. Validation (200 tokens)

   • Update status cache: 100 tokens
   • Report changes: 100 tokens

Cache Maintenance

Auto-refresh triggers:

• New framework detected → Fetch new templates
• package.json changed → Re-scan dependencies
• 30 days since last scan → Full re-audit

Cache invalidation:

# Clear stale cache (e.g., after major refactor)
rm -rf .claude/cache/cache-strategy/

Comparison: Before vs. After

Before Optimization (4,000 tokens):

1. Read all config files (800 tokens)
2. Analyze entire codebase (1,500 tokens)
3. Generate cache patterns (1,000 tokens)
4. Explain all strategies (500 tokens)
5. Implement changes (200 tokens)

After Optimization (1,200 tokens):

1. Grep for cache targets (100 tokens)
2. Load cached templates (200 tokens)
3. Apply focused strategy (400 tokens)
4. Update implementation cache (100 tokens)
5. Report changes (400 tokens)

Savings: 70% reduction (2,800 tokens saved)

Integration with Other Skills

Skill synergy caching:

{
  "triggers_webpack_optimize": ["Build tool cache needed"],
  "triggers_performance_profile": ["Measure cache hit rates"],
  "triggers_lighthouse": ["Validate cache headers"],
  "triggered_by_ci_setup": ["CI/CD cache configuration"]
}

Strategy:

• Cache skill relationships
• Avoid re-analyzing when chained
• Share framework detection results

Success Metrics

Token efficiency:

• HTTP headers only: 400-600 tokens (85% reduction)
• Redis setup only: 500-800 tokens (80% reduction)
• Service worker only: 600-1,000 tokens (75% reduction)
• Full implementation: 1,000-1,800 tokens (60% reduction)

Cache hit rates:

• Template patterns: 95% (rarely change)
• Framework detection: 90% (stable per project)
• Best practices: 98% (universal guidelines)
• Hot paths: 80% (evolve with code)

Integration Points

Synergistic Skills:

• /webpack-optimize - Build tool caching and optimization
• /performance-profile - Measure cache effectiveness
• /lighthouse - Audit cache headers and service workers
• /ci-setup - Configure cache in CI/CD pipelines

Suggests /webpack-optimize when:

• Build tool caching needs optimization
• Bundle splitting affects cache strategy

Suggests /performance-profile when:

• Need to measure cache hit rates
• Validate cache performance improvements

Safety Mechanisms

Protection Measures:

• Create git checkpoint before changes
• Test cache strategies in development
• Validate cache invalidation works
• Ensure no sensitive data cached
• Provide cache debugging instructions

Cache Debugging:

# Clear browser cache for testing
# Chrome DevTools: Application > Clear storage

# Clear Redis cache
redis-cli FLUSHDB

# Clear service worker cache
# Chrome DevTools: Application > Service Workers > Unregister

Rollback Procedure:

# Restore previous configuration
git checkout HEAD -- nginx.conf service-worker.js
# Clear problematic cache
# Rebuild and redeploy

Common Caching Scenarios

Scenario 1: Static Website

• Long-term browser caching for all assets
• CDN edge caching
• Service worker for offline support
• Immutable cache for versioned files

Scenario 2: SPA (Single Page App)

• Service worker with app shell caching
• API response caching (network-first)
• Static asset caching (cache-first)
• Stale-while-revalidate for data

Scenario 3: API Server

• Redis for database query results
• Response caching with appropriate headers
• ETags for conditional requests
• CDN for public endpoints

Scenario 4: E-commerce Site

• Product images (long cache, CDN)
• Product data (short cache, stale-while-revalidate)
• User data (no cache, private)
• Shopping cart (no cache, real-time)

Expected Results

Performance Improvements:

• 40-80% faster repeat page loads
• 50-90% reduction in API calls
• 30-60% reduction in server load
• Improved offline capabilities

Cache Hit Rates:

• Static assets: 95%+ hit rate
• API responses: 60-80% hit rate
• Database queries: 70-90% hit rate

Error Handling

If caching introduces issues:

• I'll identify the problematic cache layer
• Provide specific debugging steps
• Suggest cache invalidation methods
• Offer alternative caching strategies
• Ensure no stale data served to users

Important Notes

I will NEVER:

• Cache sensitive user data improperly
• Add AI attribution to configuration files
• Break existing cache invalidation
• Implement caching without validation
• Cache authenticated requests publicly

Best Practices:

• Always validate cache invalidation works
• Test offline functionality (service workers)
• Monitor cache hit rates
• Set appropriate TTL values
• Document caching strategy

Credits

Inspired by:

• MDN Web Docs - HTTP Caching
• Workbox Documentation
• Redis Caching Best Practices
• Web.dev - Service Worker Caching
• High-performance web application patterns

This skill helps you implement robust caching strategies that balance performance with data freshness.