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python-observability

Add structured logging, Prometheus metrics, and OpenTelemetry tracing to Python applications.

Updated Jul 7, 2026
Search Tags:#monitoring#logging#python#metrics#tracing
One-click install
npx skills add https://github.com/voidrot/agents --skill python-observability-voidrot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-observability
Source: https://github.com/voidrot/agents/tree/main/skills/python/python-observability
Command: npx skills add https://github.com/voidrot/agents --skill python-observability-voidrot

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires prometheus_client, opentelemetry, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of adding and configuring observability features such as logging, metrics, and tracing in Python applications, simplifying the process of monitoring and debugging in production.

Core Features & Use Cases

  • Structured Logging: Implements structured logging with JSON format for easier analysis.
  • Metrics Collection: Exposes metrics for monitoring performance and health.
  • Tracing: Sets up tracing to track requests and transactions across services.
  • Use Case: Use this Skill in your Python service to add structured logging and Prometheus metrics, allowing you to monitor service performance and quickly diagnose issues.

Quick Start

To add observability to your Python service, use the following commands:

  1. Install necessary observability tools like Prometheus and OpenTelemetry.
  2. Run the setup-observability.py script in your application directory.

Frequently Asked Questions about python-observability

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

FAQPage Schema
How do I add Prometheus metrics and tracing to a Python application?▼

You can add Prometheus metrics and tracing to a Python application by running the setup-observability.py script, which configures OpenTelemetry and prometheus_client to expose metrics and track requests across services.

What is the best way to implement structured logging for Python services in production?▼

The best way to implement structured logging for Python services is to use this Skill to generate JSON-formatted logs, allowing you to monitor service performance and quickly diagnose issues in production environments.

Can I use OpenTelemetry with Python to track transactions across microservices?▼

Yes, you can use OpenTelemetry with Python to set up tracing that tracks requests and transactions across services, helping you monitor application health and debug distributed workflows.

Do I need prometheus_client and opentelemetry installed to set up Python observability?▼

Yes, you need prometheus_client and opentelemetry installed as dependencies, which provide the underlying libraries for metrics collection and tracing setup required by the observability script.

How do I set up observability tools in my existing Python application directory?▼

To set up observability tools, first install necessary tools like Prometheus and OpenTelemetry, then execute the setup-observability.py script directly in your application directory to apply the monitoring configuration.

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