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python-observability

Instrument Python applications with structlog, Prometheus, and OpenTelemetry.

1|Updated Feb 19, 2026
Search Tags:#logging#python#metrics#observability#tracing#prometheus#structlog
One-click install
npx skills add https://github.com/yusufcmg/Antigravity-Agents-Workflows --skill python-observability-yusufcmg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-observability
Source: https://github.com/yusufcmg/Antigravity-Agents-Workflows/tree/main/.agent/skills/languages/python/python-observability
Command: npx skills add https://github.com/yusufcmg/Antigravity-Agents-Workflows --skill python-observability-yusufcmg

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you instrument Python applications to gain deep insights into their runtime behavior, making it easier to monitor, debug, and understand production systems.

Core Features & Use Cases

  • Structured Logging: Implement JSON-formatted logs with consistent fields for better machine readability and analysis.
  • Metrics Collection: Integrate with Prometheus to track key performance indicators like latency, traffic, and errors.
  • Distributed Tracing: Set up end-to-end tracing using OpenTelemetry to follow requests across multiple services.
  • Use Case: When a critical API endpoint starts experiencing high latency, use this Skill to add structured logging around the request, instrument the endpoint with Prometheus metrics, and ensure correlation IDs are propagated for tracing.

Quick Start

Configure structlog for JSON output with consistent fields by running the provided Python code snippet.

Frequently Asked Questions about python-observability

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

FAQPage Schema
How do I implement structured logging in Python using structlog?▼

Structured logging in Python using structlog configures JSON-formatted outputs with consistent fields to improve machine readability and runtime behavior analysis in production systems.

What is the best way to collect Python application metrics with Prometheus?▼

Collecting Python application metrics with Prometheus involves instrumenting code to track key performance indicators like latency, traffic, and errors for real-time performance monitoring.

How does distributed tracing work with OpenTelemetry in Python?▼

Distributed tracing with OpenTelemetry in Python works by propagating correlation IDs to follow requests across multiple services, enabling end-to-end visibility for debugging.

Can I monitor high latency API endpoints in Python with these observability tools?▼

Yes, you can monitor Python API latency by adding structured logging around requests, instrumenting endpoints with Prometheus metrics, and propagating correlation IDs for tracing.

Do I need dependencies to set up Python observability for production debugging?▼

No external dependencies are required to start, as this Skill provides patterns and code examples directly for instrumenting Python applications with observability tools for production debugging.

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