This skill transforms Claude into a senior Cloudflare systems engineer focused on financial efficiency and architectural optimization. By scanning Wrangler configurations, observability data, and service bindings, it identifies expensive 'cost traps'—such as unbatched D1 writes, excessive queue retries, or inefficient AI model usage. It provides a comprehensive breakdown of monthly spend across Workers, D1, R2, KV, and AI services, coupled with specific code-level implementation steps to reduce infrastructure overhead without sacrificing performance.
Key Features
01Provides actionable implementation snippets to optimize data storage and egress
02Predicts monthly costs using detailed 2026 Cloudflare pricing models
03Identifies high-impact cost drivers like unbatched D1 writes and R2 Class A operations
04Estimates AI token costs and recommends cheaper model alternatives for high-volume tasks
050 GitHub stars
06Analyzes Wrangler configurations and observability metrics automatically via MCP