How does this skill help debug Cloudflare Workers?

It allows you to query real-time logs and filter by specific fields like status codes or script names to isolate and identify the root cause of errors.

How do I find out what fields I can query?

You can use the built-in observability_keys tool to discover all available metadata and custom data fields currently present in your logs.

Can I see custom console.log output with this skill?

Yes, the skill can access and query custom fields generated from console.log output within your Workers, making it easier to track specific application logic.

Is this useful for post-deployment checks?

Absolutely. It is designed to compare error rates and performance metrics before and after deployments to ensure there are no regressions.

Does it provide performance metrics?

Yes, it allows you to calculate and analyze latency, execution times, and invocation counts to monitor worker performance and traffic patterns.

Cloudflare Workers Observability

Name: Cloudflare Workers Observability
Author: FlorinPopaCodes

byFlorinPopaCodes

분석 및 모니터링

Monitors, debugs, and analyzes Cloudflare Workers performance and errors using real-time logs and metrics.

소개

This skill empowers developers to troubleshoot Cloudflare Workers by querying live logs, analyzing error rates, and monitoring performance metrics directly through Claude. It provides a structured workflow for discovering available data fields, filtering results by status codes or routes, and correlating custom console.log output with deployment events. Whether you are identifying production bottlenecks or verifying post-deployment health, this skill provides the deep visibility needed to maintain robust serverless applications on the Cloudflare network.

주요 기능

Support for filtering custom console.log metadata
Traffic pattern analysis over specific time intervals
Dynamic data field discovery and schema exploration
Real-time log querying and invocation tracking
0 GitHub stars
Performance metric analysis including latency and error rates

사용 사례

Analyzing execution latency across different routes to identify performance bottlenecks.
Troubleshooting production errors by filtering logs for specific HTTP status codes.
Verifying deployment health by monitoring error rates immediately after a new worker release.

소개

주요 기능

Support for filtering custom console.log metadata
Traffic pattern analysis over specific time intervals
Dynamic data field discovery and schema exploration
Real-time log querying and invocation tracking
0 GitHub stars
Performance metric analysis including latency and error rates

사용 사례

Analyzing execution latency across different routes to identify performance bottlenecks.
Troubleshooting production errors by filtering logs for specific HTTP status codes.
Verifying deployment health by monitoring error rates immediately after a new worker release.