How does this skill help with Cloudflare Workers cold starts?

The skill provides specific strategies for tree-shaking, lazy loading heavy dependencies, and keeping bundle sizes under 1MB to ensure rapid startup times.

What caching strategies does this skill recommend?

It implements a multi-layer approach including request-scoped in-memory caching, edge-local Cache API storage, and global KV storage for persistent data.

Does it support the Cloudflare Workers Unbound model?

Yes, the skill provides guidance tailored to all resource models, including Free, Paid, and Unbound, with their respective CPU and memory constraints.

Is this skill useful for database interactions like D1?

Absolutely. It provides patterns for batching D1 and KV operations to minimize round-trip latency and avoid blocking the main execution thread.

Can it help resolve 'CPU limit exceeded' errors?

Yes, it includes profiling templates and optimization patterns like streaming and asynchronous processing to help workers stay within the 10ms to 50ms CPU limits.

Cloudflare Workers Performance Optimization

Name: Cloudflare Workers Performance Optimization
Author: secondsky

bysecondsky

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Infraestructura en la Nube

Optimizes Cloudflare Workers performance by managing CPU usage, memory allocation, and advanced caching strategies.

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This skill equips Claude with specialized knowledge to diagnose and resolve performance bottlenecks in Cloudflare Workers. It provides implementation patterns for reducing cold starts through bundle optimization, implementing multi-layer caching via the Cache API, and leveraging streaming to handle large payloads efficiently. It is designed for developers building production-grade serverless applications that must stay within strict CPU and memory limits while maintaining ultra-low global latency.

Características Principales

CPU profiling and hot-path optimization techniques
Cold start mitigation through bundle size and import management
Multi-layer caching patterns using Memory, Cache API, and KV
Streaming response implementations to prevent memory pressure
Batching strategies for D1 and KV database operations
21 GitHub stars

Casos de Uso

Resolving 'CPU Limit Exceeded' and worker timeout errors
Reducing global latency for serverless API endpoints
Optimizing heavy data processing tasks at the edge

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Características Principales

CPU profiling and hot-path optimization techniques
Cold start mitigation through bundle size and import management
Multi-layer caching patterns using Memory, Cache API, and KV
Streaming response implementations to prevent memory pressure
Batching strategies for D1 and KV database operations
21 GitHub stars

Casos de Uso

Resolving 'CPU Limit Exceeded' and worker timeout errors
Reducing global latency for serverless API endpoints
Optimizing heavy data processing tasks at the edge