Does this skill help with LLM-specific monitoring?

Yes, it includes specialized patterns for Langfuse integration to track LLM token usage, execution costs, and trace AI-specific workflows.

What metrics collection standard does this skill use?

It primarily implements the RED method (Rate, Errors, Duration) and the Golden Signals approach using Prometheus-compatible metrics.

Can it help with Kubernetes health management?

Yes, it provides standard implementation templates for Liveness, Readiness, and Startup probes to ensure healthy container orchestration.

How does it handle high-traffic logging volume?

The skill provides log sampling strategies and structured JSON patterns to ensure you capture critical data without overwhelming your storage or aggregation tools.

What are the 'Three Pillars of Observability' this skill supports?

The skill focuses on Logs (recording specific events), Metrics (measuring performance over time), and Traces (tracking the flow of requests through services).

Observability & Monitoring

Name: Observability & Monitoring
Author: yonatangross

byyonatangross

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Analíticas y Monitorización

Implements comprehensive logging, metrics, distributed tracing, and alerting systems to ensure application reliability and performance.

Acerca de

This skill equips Claude with the expertise of a metrics architect to design and implement robust observability frameworks within your applications. It covers the three pillars of observability—structured logging with correlation IDs, Prometheus-based metrics collection using the RED method, and distributed tracing via OpenTelemetry. Whether you are debugging complex production issues, setting up Grafana dashboards, or monitoring LLM costs and token usage with Langfuse, this skill provides production-ready patterns to ensure your systems remain transparent and resilient.

Características Principales

Pre-configured alerting strategies and health check endpoints for Kubernetes environments.
Prometheus metrics implementation focusing on the RED (Rate, Errors, Duration) method.
Distributed tracing setup using OpenTelemetry for deep request flow visibility.
LLM-specific observability integration with Langfuse for tracking token usage and costs.
Structured JSON logging with correlation IDs for seamless cross-service request tracking.
29 GitHub stars

Casos de Uso

Debugging high-latency issues or intermittent error rates in production environments.
Architecting a complete monitoring stack for a new microservices-based application.
Implementing performance and cost tracking for AI applications utilizing LLMs.

Acerca de

Características Principales

Pre-configured alerting strategies and health check endpoints for Kubernetes environments.
Prometheus metrics implementation focusing on the RED (Rate, Errors, Duration) method.
Distributed tracing setup using OpenTelemetry for deep request flow visibility.
LLM-specific observability integration with Langfuse for tracking token usage and costs.
Structured JSON logging with correlation IDs for seamless cross-service request tracking.
29 GitHub stars

Casos de Uso

Debugging high-latency issues or intermittent error rates in production environments.
Architecting a complete monitoring stack for a new microservices-based application.
Implementing performance and cost tracking for AI applications utilizing LLMs.