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python-observability

Instrument Python services with structured JSON logging, metrics, and OpenTelemetry tracing.

Updated Feb 3, 2026
Search Tags:#logging#python#metrics#observability#tracing#open-telemetry#correlation-id
One-click install
npx skills add https://github.com/leonardoteodoroo/amino-advanced --skill python-observability-leonardoteodoroo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-observability
Source: https://github.com/leonardoteodoroo/amino-advanced/tree/main/.agent/skills/python-observability
Command: npx skills add https://github.com/leonardoteodoroo/amino-advanced --skill python-observability-leonardoteodoroo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Python observability patterns provide structured logging, metrics, and tracing to quickly diagnose production issues without redeploying code.

Core Features & Use Cases

  • Structured logging with JSON.
  • Metrics collection and tracing integration.
  • Propagate correlation IDs and build observability dashboards.
  • Real-world scenario example.

Quick Start

Enable structured logging, metrics collection, and distributed tracing in a Python service with a minimal configuration and run it in production.

Frequently Asked Questions about python-observability

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

FAQPage Schema
How do I implement Python observability with logs, metrics, and traces?▼

Python observability is implemented by applying structured JSON logging, bounded cardinality metrics, and OpenTelemetry tracing integration to instrument services and diagnose production issues without redeploying code.

What is structured logging and how does it work with Python microservices?▼

Structured logging formats Python application logs as JSON objects, enabling consistent data parsing across microservices and facilitating the propagation of correlation IDs to track requests end-to-end through distributed systems.

How do I propagate correlation IDs across Python microservices?▼

Correlation ID propagation across Python microservices is achieved by instrumenting services with OpenTelemetry tracing patterns, ensuring unique request identifiers are passed between services to build unified observability dashboards.

Can I use OpenTelemetry tracing integration with Python applications in production?▼

OpenTelemetry tracing integration supports Python applications in both development and production environments, prescribing patterns like bounded cardinality for metrics to prevent excessive label combinations from overwhelming your observability backend.

What is the best way to collect metrics with bounded cardinality in Python?▼

Collecting metrics with bounded cardinality in Python involves restricting label values to finite sets during instrumentation, preventing metric explosion while maintaining structured logging and tracing patterns for comprehensive application observability.

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