What tracing standards are used?

This skill follows the OpenTelemetry (OTel) standard for distributed tracing, metrics, and OTLP protocol exports.

Does this skill support Python applications?

Yes, it includes specific instrumentation patterns for Python, FastAPI, and the Requests library using OpenTelemetry.

How does this help with debugging distributed systems?

By implementing correlation IDs and structured logs, it allows you to trace a single request as it travels through multiple microservices.

Can I use this for Azure Monitor?

Absolutely, it provides dedicated YAML configurations for integrating Dapr with Azure Monitor and Application Insights.

Dapr Observability Setup

Name: Dapr Observability Setup
Author: Sahib-Sawhney-WH

bySahib-Sawhney-WH

•

Analytics & Monitoring

Configures comprehensive OpenTelemetry tracing, Prometheus metrics, and structured logging for Dapr-based microservices.

This skill provides expert guidance for implementing full-stack observability in Dapr applications, enabling developers to monitor distributed systems effectively. It automates the configuration of OpenTelemetry for tracing, Prometheus for metrics collection, and JSON-based structured logging, while offering seamless integration patterns for backends like Azure Monitor and Jaeger. Whether you are setting up a new microservice or debugging complex distributed issues, this skill ensures your Dapr environment follows industry best practices for system visibility and performance monitoring.

Key Features

01Real-time metrics collection using Prometheus and OTLP

02Ready-to-use OpenTelemetry Collector pipeline setups

03Distributed tracing configuration with OpenTelemetry and Jaeger

04Structured JSON logging implementation for service-wide consistency

053 GitHub stars

06Dapr-specific configuration templates for Azure Monitor and local environments

Use Cases

01Troubleshooting performance bottlenecks in distributed request paths

02Integrating cloud-native observability backends like Azure Application Insights

03Setting up monitoring for a new Dapr-based microservices architecture

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sahib-sawhney-wh/sahibs-claude-plugin-marketplace observability-setup

For use in Claude.ai and ChatGPT

Key Features

01Real-time metrics collection using Prometheus and OTLP

02Ready-to-use OpenTelemetry Collector pipeline setups

03Distributed tracing configuration with OpenTelemetry and Jaeger

04Structured JSON logging implementation for service-wide consistency

053 GitHub stars

06Dapr-specific configuration templates for Azure Monitor and local environments

Use Cases

01Troubleshooting performance bottlenecks in distributed request paths

02Integrating cloud-native observability backends like Azure Application Insights

03Setting up monitoring for a new Dapr-based microservices architecture

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sahib-sawhney-wh/sahibs-claude-plugin-marketplace observability-setup

For use in Claude.ai and ChatGPT