How does this skill help with Cloudflare D1 storage limits?

It provides strategies for time-based and entity-based sharding, allowing you to partition data across multiple D1 instances or archive older data to R2.

Can it help solve the 1 write per second limit on KV keys?

Yes, it implements key-based sharding patterns that distribute high-frequency writes across multiple shard keys to increase total throughput.

Does this skill provide guidance on Worker memory management?

Absolutely. It includes patterns for streaming large payloads and using Durable Objects for chunked processing to stay within the 128MB limit.

Is cost optimization considered in these scaling strategies?

Yes, the skill includes a cost implications guide to help users choose strategies like Tiered Cache that optimize performance without unnecessary overhead.

Cloudflare Architecture Scaling

Name: Cloudflare Architecture Scaling
Author: majiayu000

bymajiayu000

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Cloud Infrastructure

Implements advanced scaling strategies for Cloudflare architectures, including sharding, caching, and read-replication patterns.

About

This skill provides Claude with expert-level guidance on scaling Cloudflare-based applications beyond default platform limits. It offers a comprehensive framework for identifying and resolving bottlenecks across D1, KV, R2, and Workers, providing concrete implementation patterns for write-through caching, multi-region coordination, and data sharding. Whether you're preparing for massive traffic growth or optimizing for cost-efficiency, this skill equips Claude to recommend and implement production-ready scaling solutions tailored specifically to the Cloudflare ecosystem.

Key Features

Comprehensive Scaling Decision Matrix for identifying resource bottlenecks
Advanced KV caching patterns including write-through and stale-while-revalidate
Database sharding strategies for high-volume D1 and KV workloads
Multi-region coordination patterns using Durable Objects and Smart Placement
Queue optimization techniques for high-throughput background processing
64 GitHub stars

Use Cases

Implementing memory-efficient stream processing for large R2 data payloads
Optimizing high-traffic applications hitting Cloudflare KV or D1 throughput limits
Designing multi-region architectures for low-latency global data access

About

Key Features

Comprehensive Scaling Decision Matrix for identifying resource bottlenecks
Advanced KV caching patterns including write-through and stale-while-revalidate
Database sharding strategies for high-volume D1 and KV workloads
Multi-region coordination patterns using Durable Objects and Smart Placement
Queue optimization techniques for high-throughput background processing
64 GitHub stars

Use Cases

Implementing memory-efficient stream processing for large R2 data payloads
Optimizing high-traffic applications hitting Cloudflare KV or D1 throughput limits
Designing multi-region architectures for low-latency global data access