Can I use this to optimize my Cloudflare bill?

Yes, the skill includes probes for analyzing AI Gateway cost-by-model, worker CPU time analysis, and cache hit rates to identify specific cost-saving opportunities.

What happens if MCP tools are not configured?

The skill features graceful degradation, where it will note that MCP tools are unavailable and continue with static code analysis while tagging findings appropriately.

How does this skill handle database performance?

It uses MCP tools to run EXPLAIN QUERY PLAN on live D1 databases, identifying full table scans, missing indexes, and temporary sort operations.

What are Cloudflare Audit Probes?

Cloudflare Audit Probes are pre-built diagnostic query patterns that allow Claude to validate live production data from Cloudflare services like D1, R2, KV, and Workers.

How does it track the accuracy of its findings?

It uses provenance tags like [LIVE-VALIDATED], [LIVE-REFUTED], and [STATIC] to clearly distinguish between code-level assumptions and actual production evidence.

Cloudflare Audit Probes

Name: Cloudflare Audit Probes
Author: littlebearapps

bylittlebearapps

0•

분석 및 모니터링

Conducts live diagnostic audits of Cloudflare services to validate infrastructure performance, costs, and security against production data.

The Cloudflare Audit Probes skill empowers Claude with specialized diagnostic queries to bridge the gap between static code analysis and live production environments. It enables real-time validation of D1 database indexes, worker observability metrics, AI Gateway cost distributions, and queue health through MCP (Model Context Protocol) tool orchestration. This skill is essential for engineers who need to perform deep-dive audits, optimize serverless costs, and verify that infrastructure configurations are performing as intended in the wild by comparing static findings against live-validated results.

주요 기능

01Queue Health and Dead-Letter Queue (DLQ) Configuration Checks

02Automated MCP Connectivity Verification and Graceful Degradation

03D1 Database Query Plan Analysis and Schema Discovery

040 GitHub stars

05AI Gateway Cost, Token Usage, and Cache Hit Rate Auditing

06Real-time Worker Error Rate and Latency Monitoring

사용 사례

01Identifying performance bottlenecks in D1 databases using live EXPLAIN QUERY PLAN data

02Comparing static migration files against live production schemas to detect configuration drift

03Auditing AI Gateway usage to identify cost-saving opportunities through prompt caching

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add littlebearapps/cloudflare-engineer probes

For use in Claude.ai and ChatGPT

Download Skill