Claude-skill-registry edge-performance-optimizer
Automatically optimizes Cloudflare Workers performance during development, focusing on cold starts, bundle size, edge caching, and global latency
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/edge-performance-optimizer" ~/.claude/skills/majiayu000-claude-skill-registry-edge-performance-optimizer && rm -rf "$T"
manifest:
skills/data/edge-performance-optimizer/SKILL.mdsource content
Edge Performance Optimizer SKILL
Activation Patterns
This SKILL automatically activates when:
- New dependencies are added to package.json
- Large files or heavy imports are detected
- Sequential operations that could be parallelized
- Missing edge caching opportunities
- Bundle size increases significantly
- Storage operations without optimization patterns
Expertise Provided
Edge-Specific Performance Optimization
- Cold Start Optimization: Minimizes bundle size and heavy dependencies
- Global Distribution: Ensures edge caching for global performance
- CPU Time Optimization: Identifies CPU-intensive operations
- Storage Performance: Optimizes KV/R2/D1 access patterns
- Parallel Operations: Suggests parallelization opportunities
- Bundle Analysis: Monitors and optimizes bundle size
Specific Checks Performed
❌ Performance Anti-Patterns
// These patterns trigger immediate alerts: import axios from 'axios'; // Heavy dependency (13KB) import moment from 'moment'; // Heavy dependency (68KB) import _ from 'lodash'; // Heavy dependency (71KB) // Sequential operations that could be parallel const user = await env.USERS.get(id); const settings = await env.SETTINGS.get(id); const prefs = await env.PREFS.get(id);
✅ Performance Best Practices
// These patterns are validated as correct: // Native Web APIs instead of heavy libraries const response = await fetch(url); // Built-in fetch (0KB) const now = new Date(); // Native Date (0KB) // Parallel operations const [user, settings, prefs] = await Promise.all([ env.USERS.get(id), env.SETTINGS.get(id), env.PREFS.get(id), ]);
Integration Points
Complementary to Existing Components
- edge-performance-oracle agent: Handles comprehensive performance analysis, SKILL provides immediate optimization
- workers-runtime-validator SKILL: Complements runtime checks with performance optimization
- es-deploy command: SKILL ensures performance standards before deployment
Escalation Triggers
- Complex performance architecture questions →
agentedge-performance-oracle - Global distribution strategy →
agentcloudflare-architecture-strategist - Performance troubleshooting →
agentedge-performance-oracle
Validation Rules
P1 - Critical (Performance Killer)
- Large Dependencies: Heavy libraries like moment, lodash, axios
- Bundle Size: Over 200KB (kills cold start performance)
- Sequential Operations: Multiple sequential storage/network calls
- Missing Edge Caching: No caching for frequently accessed data
P2 - High (Performance Impact)
- Bundle Size: Over 100KB (slows cold starts)
- CPU Time: Operations approaching 50ms limit
- Lazy Loading: Dynamic imports that hurt cold start
- Large Payloads: Responses over 100KB without streaming
P3 - Medium (Optimization Opportunity)
- Bundle Size: Over 50KB (could be optimized)
- Missing Parallelization: Operations that could be parallel
- No Request Caching: Repeated expensive operations
Remediation Examples
Fixing Heavy Dependencies
// ❌ Critical: Heavy dependencies (150KB+ bundle) import axios from 'axios'; // 13KB import moment from 'moment'; // 68KB import _ from 'lodash'; // 71KB // Total: 152KB just for utilities! // ✅ Correct: Native Web APIs (minimal bundle) // Use fetch instead of axios const response = await fetch(url); const data = await response.json(); // Use native Date instead of moment const now = new Date(); const tomorrow = new Date(Date.now() + 86400000); // Use native methods instead of lodash const unique = [...new Set(array)]; const grouped = array.reduce((acc, item) => { const key = item.category; if (!acc[key]) acc[key] = []; acc[key].push(item); return acc; }, {}); // Total: < 5KB for utilities
Fixing Sequential Operations
// ❌ High: Sequential KV operations (3x network round-trips) export default { async fetch(request: Request, env: Env) { const user = await env.USERS.get(userId); // 10-30ms const settings = await env.SETTINGS.get(id); // 10-30ms const prefs = await env.PREFS.get(id); // 10-30ms // Total: 30-90ms just for storage! } } // ✅ Correct: Parallel operations (single round-trip) export default { async fetch(request: Request, env: Env) { const [user, settings, prefs] = await Promise.all([ env.USERS.get(userId), env.SETTINGS.get(id), env.PREFS.get(id), ]); // Total: 10-30ms (single round-trip) } }
Fixing Missing Edge Caching
// ❌ Critical: No edge caching (slow globally) export default { async fetch(request: Request, env: Env) { const config = await fetch('https://api.example.com/config'); // Every request goes to origin! // Sydney user → US origin = 200ms+ just for config } } // ✅ Correct: Edge caching pattern export default { async fetch(request: Request, env: Env) { const cache = caches.default; const cacheKey = new Request('https://example.com/config', { method: 'GET' }); // Try cache first let response = await cache.match(cacheKey); if (!response) { // Cache miss - fetch from origin response = await fetch('https://api.example.com/config'); // Cache at edge with 1-hour TTL response = new Response(response.body, { ...response, headers: { ...response.headers, 'Cache-Control': 'public, max-age=3600', } }); await cache.put(cacheKey, response.clone()); } // Sydney user → Sydney edge cache = < 10ms return response; } }
Fixing CPU Time Issues
// ❌ High: Large synchronous processing (CPU time bomb) export default { async fetch(request: Request, env: Env) { const users = await env.DB.prepare('SELECT * FROM users').all(); // If 10,000 users, this loops for 100ms+ CPU time const enriched = users.results.map(user => { return { ...user, fullName: `${user.firstName} ${user.lastName}`, // ... expensive computations }; }); } } // ✅ Correct: Bounded operations export default { async fetch(request: Request, env: Env) { // Option 1: Limit at database level const users = await env.DB.prepare( 'SELECT * FROM users LIMIT ? OFFSET ?' ).bind(10, offset).all(); // Only 10 users, bounded CPU // Option 2: Stream processing for large datasets const { readable, writable } = new TransformStream(); // Process in chunks without loading everything into memory // Option 3: Offload to Durable Object const id = env.PROCESSOR.newUniqueId(); const stub = env.PROCESSOR.get(id); return stub.fetch(request); // DO can run longer } }
MCP Server Integration
When Cloudflare MCP server is available:
- Query real performance metrics (cold start times, CPU usage)
- Analyze global latency by region
- Get latest performance optimization techniques
- Check bundle size impact on cold starts
Benefits
Immediate Impact
- Faster Cold Starts: Reduces bundle size and heavy dependencies
- Better Global Performance: Ensures edge caching for worldwide users
- Lower CPU Usage: Identifies and optimizes CPU-intensive operations
- Reduced Latency: Parallelizes operations and adds caching
Long-term Value
- Consistent Performance Standards: Ensures all code meets performance targets
- Better User Experience: Faster response times globally
- Cost Optimization: Reduced CPU time usage lowers costs
Usage Examples
During Dependency Addition
// Developer types: npm install moment // SKILL immediately activates: "❌ CRITICAL: moment is 68KB and will slow cold starts. Use native Date instead for 0KB impact."
During Storage Operations
// Developer types: sequential KV gets // SKILL immediately activates: "⚠️ HIGH: Sequential KV operations detected. Use Promise.all() to parallelize and reduce latency by 3x."
During API Development
// Developer types: fetch without caching // SKILL immediately activates: "⚠️ HIGH: No edge caching for API call. Add Cache API to serve from edge locations globally."
Performance Targets
Bundle Size
- Excellent: < 10KB
- Good: < 50KB
- Acceptable: < 100KB
- Needs Improvement: > 100KB
- Action Required: > 200KB
Cold Start Time
- Excellent: < 3ms
- Good: < 5ms
- Acceptable: < 10ms
- Needs Improvement: > 10ms
- Action Required: > 20ms
Global Latency (P95)
- Excellent: < 100ms
- Good: < 200ms
- Acceptable: < 500ms
- Needs Improvement: > 500ms
- Action Required: > 1000ms
This SKILL ensures Workers performance by providing immediate, autonomous optimization of performance patterns, preventing common performance issues and ensuring fast global response times.