What is metric cardinality and why does it matter?

Cardinality refers to the number of unique label combinations. High cardinality (like using User IDs as labels) can crash your metrics server; this skill teaches how to keep labels bounded.

Does this skill support OpenTelemetry?

Yes, it includes patterns for setting up OpenTelemetry tracing to track requests as they flow through various spans and downstream services.

Why should I use structured logging instead of standard Python logging?

Structured logging (JSON) allows logs to be easily parsed and queried by log management systems like ELK or Datadog, enabling better alerting and diagnostic filtering than plain text logs.

What are the 'Four Golden Signals' mentioned in this skill?

The Four Golden Signals are Latency, Traffic, Errors, and Saturation. Monitoring these provides a high-level overview of service health and user experience.

Python Observability

Name: Python Observability
Author: JantonioFC

byJantonioFC

•

Analytics & Monitoring

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

The Python Observability skill provides a standardized framework for instrumenting Python services to ensure maximum visibility into system behavior. It facilitates the implementation of structured JSON logging using Structlog, the tracking of the 'Four Golden Signals' via Prometheus metrics, and end-to-end distributed tracing with OpenTelemetry. By following these patterns, developers can debug production issues, monitor service health, and propagate correlation IDs across request chains without needing to deploy new code for every diagnostic requirement.

Key Features

01Correlation ID propagation for cross-service request tracking

022 GitHub stars

03Prometheus integration for latency, traffic, and error metrics

04Standardized semantic log levels and timing context managers

05Structured JSON logging for machine-readable diagnostics

06Distributed tracing implementation with OpenTelemetry

Use Cases

01Building real-time monitoring dashboards for service performance and saturation

02Establishing consistent logging standards across multiple Python development teams

03Debugging complex production failures in distributed microservices

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank python-observability

For use in Claude.ai and ChatGPT

Key Features

01Correlation ID propagation for cross-service request tracking

022 GitHub stars

03Prometheus integration for latency, traffic, and error metrics

04Standardized semantic log levels and timing context managers

05Structured JSON logging for machine-readable diagnostics

06Distributed tracing implementation with OpenTelemetry

Use Cases

01Building real-time monitoring dashboards for service performance and saturation

02Establishing consistent logging standards across multiple Python development teams

03Debugging complex production failures in distributed microservices

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jantoniofc/skillsbank python-observability

For use in Claude.ai and ChatGPT