How does the advisor improve KV latency?

It identifies sequential await operations that trigger multiple network round-trips and suggests refactoring them into parallel Promise.all calls, which can reduce storage latency by 3x or more.

Does it provide guidance on data consistency?

Yes, it helps you decide between KV (eventually consistent), R2 (strongly consistent), and D1 (relational/SQL) based on your specific application requirements.

Can this skill help reduce my Cloudflare bill?

Yes, by suggesting request-scoped caching and identifying redundant calls, the skill helps you minimize the total number of billable KV read/write operations.

When does it recommend moving data from KV to R2?

The skill triggers a high-priority alert when it detects large values (typically >1MB) being stored in KV, suggesting R2 as a better choice for large objects and files.

KV Optimization Advisor

Name: KV Optimization Advisor
Author: hirefrank

byhirefrank

•

Database Management

Optimizes Cloudflare KV storage patterns by identifying performance anti-patterns and recommending efficient data access strategies.

About

The KV Optimization Advisor is a specialized skill for Claude Code designed to enhance Cloudflare Workers development through real-time performance tuning of KV storage operations. It automatically detects sequential network calls that should be parallelized, identifies missing caching opportunities, and monitors for large data patterns that could exceed storage limits. By offering contextual guidance on choosing between KV, R2, and D1, this skill ensures your edge applications remain fast, cost-effective, and scalable while adhering to best practices for high-performance serverless architecture.

Key Features

Request-scoped caching implementation recommendations
TTL optimization and batch operation strategies
Real-time monitoring for value size and storage limits
Automatic parallelization of sequential KV operations
Storage choice guidance between KV, R2, and D1
2 GitHub stars

Use Cases

Refactoring legacy storage code to follow modern edge performance standards
Reducing latency in high-traffic Cloudflare Workers applications
Determining the most cost-effective storage medium for new data features

About

Key Features

Request-scoped caching implementation recommendations
TTL optimization and batch operation strategies
Real-time monitoring for value size and storage limits
Automatic parallelization of sequential KV operations
Storage choice guidance between KV, R2, and D1
2 GitHub stars

Use Cases

Refactoring legacy storage code to follow modern edge performance standards
Reducing latency in high-traffic Cloudflare Workers applications
Determining the most cost-effective storage medium for new data features