observability-principles

Name: observability-principles
Availability: InStock
Author: mdproctor

Establish production observability across logs, traces, and metrics.

1|5|Updated Mar 29, 2026

Search Tags:#logging #metrics #observability #tracing #mdc #correlation-id #header-propagation

Ready to run

npx skills add https://github.com/mdproctor/claude-skills --skill observability-principles

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: observability-principles
Source: https://github.com/mdproctor/claude-skills/tree/main/observability-principles
Command: npx skills add https://github.com/mdproctor/claude-skills --skill observability-principles

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Production systems often suffer from slow incident diagnosis due to fragmented observability. Establishing universal observability across logs, traces, and metrics helps teams understand system behavior at scale and reduces mean time to resolution.

Core Features & Use Cases

Structured logging for consistent, machine-readable events with contextual metadata.
Distributed tracing to follow requests across services and identify bottlenecks.
Standardized metrics to measure health, performance, and reliability.
MDC/correlation ID patterns and header propagation for end-to-end traceability.
Production-ready guidance for OpenTelemetry integration and instrumentation.

Quick Start

Configure your services to enable structured logging, propagate correlation IDs, and enable tracing and metrics collection using OpenTelemetry.

Frequently Asked Questions about observability-principles

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I implement distributed tracing for multi-service architectures?▼

Distributed tracing follows requests across multi-service architectures using OpenTelemetry-based instrumentation and header propagation to identify bottlenecks. It ensures end-to-end traceability by applying consistent tracing patterns across development, testing, and production environments.

How do I propagate correlation IDs across services for end-to-end traceability?▼

Correlation ID propagation ensures end-to-end traceability by applying MDC patterns and header propagation across multi-service architectures. This establishes consistent logging with contextual metadata so distributed requests can be tracked seamlessly between services.

What's the best way to standardize metrics naming and exposure for production systems?▼

Standardized metrics naming and exposure measures system health, performance, and reliability across cloud-native apps. It enforces consistent metrics implementations in development, testing, and production to help teams understand system behavior at scale and reduce mean time to resolution.

Does this observability guidance work with OpenTelemetry integration?▼

OpenTelemetry integration provides production-ready guidance for instrumentation across logs, traces, and metrics. It supports cloud-native apps and distributed systems by enabling consistent collection and standardized exposure for comprehensive observability.

Why do I need structured logging for incident diagnosis in distributed systems?▼

Structured logging creates consistent, machine-readable events with contextual metadata to accelerate incident diagnosis in distributed systems. It replaces fragmented observability with universal patterns, reducing mean time to resolution across multi-service architectures.

Can I use these observability patterns for cloud-native apps in production?▼

These observability patterns apply directly to cloud-native apps in production, establishing universal logging, tracing, and metrics. They guide consistent implementations across development, testing, and production environments for multi-service architectures and distributed systems.