What are the core pillars of observability covered?

The skill implements three essential pillars: Logs (discrete event records), Metrics (aggregate quantitative data), and Traces (end-to-end request flows).

Is this skill suitable for non-production code?

While it can be used anywhere, it is most valuable for production systems where reliability, performance monitoring, and rapid incident response are critical requirements.

What programming languages does this skill support?

While the specific code examples focus on Go and its slog library, the architectural patterns for structured logging, metrics, and tracing are 90-95% transferable to Python, Java, Node.js, and Rust.

How does this improve debugging efficiency?

By implementing structured logging with request ID propagation, developers can filter logs instantly and trace a single request's path across services, resulting in a measured 23-46x speedup in diagnostic time.

Does this skill work with existing monitoring stacks?

Yes, it follows industry standards like Prometheus-style exposition and OpenTelemetry context propagation, making it compatible with tools like Grafana, Jaeger, and Datadog.

Observability Instrumentation

Name: Observability Instrumentation
Author: yaleh

byyaleh

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分析と監視

Implements the three pillars of observability—structured logging, metrics, and distributed tracing—to provide deep visibility into application health and performance.

概要

This skill equips Claude with a comprehensive methodology for integrating production-grade observability into software projects. By leveraging structured logging with Go's slog, Prometheus-style metrics, and OpenTelemetry tracing patterns, it transforms application telemetry from unstructured text into actionable data. It is specifically designed to accelerate debugging by up to 46x and facilitate performance optimization through quantitative measurement, making it indispensable for developers building reliable, scalable systems that require strict SLO/SLA tracking.

主な機能

Structured logging implementation using Go slog with context and attribute management
Quantitative metrics collection including counter, gauge, and histogram patterns
Distributed tracing setup for end-to-end request tracking and bottleneck identification
Request ID propagation patterns for correlating logs across service boundaries
Performance-optimized instrumentation valid for high-concurrency production environments
15 GitHub stars

ユースケース

Accelerating root-cause analysis for complex production issues through distributed tracing
Transitioning from unstructured printf-style debugging to production-grade observability
Implementing real-time performance monitoring and reliability tracking for microservices

概要

主な機能

Structured logging implementation using Go slog with context and attribute management
Quantitative metrics collection including counter, gauge, and histogram patterns
Distributed tracing setup for end-to-end request tracking and bottleneck identification
Request ID propagation patterns for correlating logs across service boundaries
Performance-optimized instrumentation valid for high-concurrency production environments
15 GitHub stars

ユースケース

Accelerating root-cause analysis for complex production issues through distributed tracing
Transitioning from unstructured printf-style debugging to production-grade observability
Implementing real-time performance monitoring and reliability tracking for microservices