Does this skill work with any programming language?

Yes. While the documentation provides Java examples, the principles of structured logging, metric naming, and health checks are universal to all backend development.

How does this skill help with debugging production issues?

It implements structured logging and correlation IDs, allowing you to trace a single request flow across multiple services and search logs by specific metadata like order IDs.

What is the difference between liveness and readiness checks in this skill?

Liveness checks determine if the application process is running, while readiness checks determine if the application is fully initialized and capable of handling traffic.

Why does the skill suggest alerting on symptoms rather than causes?

Symptom-based alerting (like high error rates) ensures you are notified when users are actually impacted, reducing 'alert fatigue' caused by internal system fluctuations that don't affect performance.

Observability Fundamentals

Name: Observability Fundamentals
Author: violetio

byvioletio

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Analíticas y Monitorización

Implements industry-standard logging, metrics, and distributed tracing patterns to ensure system reliability and monitoring.

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The Observability Fundamentals skill equips Claude Code with deep knowledge of system monitoring best practices across the three pillars of logs, metrics, and traces. It enables the AI to guide developers in implementing structured logging, defining context-rich error handling patterns, setting up correlation IDs for distributed tracing, and establishing robust health check endpoints. This skill is essential for building production-grade applications that are easy to debug, monitor, and scale using modern DevOps and SRE principles.

Características Principales

Metric naming conventions and alerting severity guidelines
Distributed tracing setup using correlation IDs and span contexts
Standardized patterns for logs, metrics, and traces (The Three Pillars)
Health check patterns for Kubernetes liveness and readiness probes
Structured logging implementation with context-rich error handling
1 GitHub stars

Casos de Uso

Transforming unstructured print statements into production-ready structured logs
Designing comprehensive API error response formats with unique request IDs
Configuring Prometheus-style metrics and symptom-based alerting for microservices

Acerca de

Características Principales

Metric naming conventions and alerting severity guidelines
Distributed tracing setup using correlation IDs and span contexts
Standardized patterns for logs, metrics, and traces (The Three Pillars)
Health check patterns for Kubernetes liveness and readiness probes
Structured logging implementation with context-rich error handling
1 GitHub stars

Casos de Uso

Transforming unstructured print statements into production-ready structured logs
Designing comprehensive API error response formats with unique request IDs
Configuring Prometheus-style metrics and symptom-based alerting for microservices