What libraries does this skill support?

It primarily supports structlog for structured logging, prometheus-client for metrics collection, and opentelemetry-api for distributed tracing.

Is this skill compatible with older Python versions?

This skill is optimized for Python 3.10 and later, as it utilizes modern features like ContextVars for context propagation.

Does it work with FastAPI?

Yes, it includes specific patterns and middleware examples for integrating observability into FastAPI and other modern Python web frameworks.

Can I use this for distributed tracing?

Absolutely. It provides implementation patterns for OpenTelemetry to track and visualize requests across different services in a microservices architecture.

How does it handle correlation IDs?

It uses Python's ContextVars to ensure request IDs are bound to the execution context and automatically included in all logs and traces.

Python Observability Patterns

Name: Python Observability Patterns
Author: 0xDarkMatter

by0xDarkMatter

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Analytics & Monitoring

Implements robust structured logging, metrics collection, and distributed tracing for production-ready Python applications.

About

The Python Observability Patterns skill equips Claude with the expertise to implement comprehensive monitoring strategies using industry-standard tools like structlog, Prometheus, and OpenTelemetry. It provides standardized patterns for request context propagation, manual and automatic instrumentation, and structured JSON logging. This skill is essential for developers building scalable Python services who need to ensure deep visibility into system performance, error tracking, and request flow across distributed architectures, especially when working with FastAPI or asynchronous frameworks.

Key Features

4 GitHub stars
Production-ready middleware patterns for web framework integration
Request context and correlation ID propagation for async tasks
Standardized structured logging with structlog and JSON rendering
Distributed tracing implementation via OpenTelemetry SDK
Prometheus metrics integration for counters, gauges, and histograms

Use Cases

Standardizing logging formats across multiple Python microservices
Monitoring API performance and latency with Prometheus dashboards
Debugging complex distributed systems using trace IDs and structured logs

About

Key Features

4 GitHub stars
Production-ready middleware patterns for web framework integration
Request context and correlation ID propagation for async tasks
Standardized structured logging with structlog and JSON rendering
Distributed tracing implementation via OpenTelemetry SDK
Prometheus metrics integration for counters, gauges, and histograms

Use Cases

Standardizing logging formats across multiple Python microservices
Monitoring API performance and latency with Prometheus dashboards
Debugging complex distributed systems using trace IDs and structured logs