Logging & Observability

About

This skill equips Claude with the domain-specific knowledge required to build highly observable production systems. It provides standardized implementations for JSON-structured logging, distributed tracing with correlation IDs, and metrics collection using OpenTelemetry or custom registries. By providing templates for both Python and TypeScript, it helps developers move beyond basic console logs to sophisticated monitoring architectures that support better debugging, performance analysis, and incident response in distributed environments.

Key Features

• Metrics collection primitives including Counters, Gauges, and Histograms
• Distributed tracing implementation with correlation IDs and spans
• Standardized log level definitions and best practice usage
• Structured JSON logging patterns for Python and TypeScript
• 0 GitHub stars
• OpenTelemetry integration and auto-instrumentation guides

Use Cases

• Setting up custom application metrics to monitor business logic and system health
• Modernizing legacy logging systems to structured JSON for tools like ELK or Datadog
• Implementing request tracing across microservices to identify performance bottlenecks