production-debugging

What problem does it solve?

This Skill provides a systematic approach to diagnosing and resolving complex issues that arise in live production environments, minimizing downtime and impact.

Core Features & Use Cases

• Hypothesis-Driven Debugging: Guides users through a structured loop of forming, gathering evidence for, and validating hypotheses to pinpoint root causes.
• Log, Metric, and Trace Analysis: Offers methods and examples for querying structured logs (LogQL, Elasticsearch DSL, CloudWatch Insights), correlating metrics, and analyzing distributed traces to understand system behavior.
• Live Debugging & Profiling: Provides practical commands for live debugging in Kubernetes and Linux environments, along with tools for profiling CPU, memory, and goroutine leaks in various runtimes (Python, JVM, Go, Node.js).
• Database & Rollback Guidance: Includes strategies for debugging database performance and lock contention, and a decision matrix for when to rollback a deployment.
• Use Case: When a critical service experiences a sudden spike in error rates, this Skill can be activated to systematically analyze logs, metrics, and traces around the time of the incident, correlating the spike with recent deployments or configuration changes to identify the faulty component.

Quick Start

Activate the production-debugging skill to analyze recent errors by correlating logs and metrics from the last hour.