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python-observability

Instrument Python applications with structured logging, metrics, and distributed tracing.

1|Updated Jul 24, 2025
Search Tags:#logging#python#metrics#observability#tracing#opentelemetry#correlation-id
One-click install
npx skills add https://github.com/civictechdc/votecatcher --skill python-observability-civictechdc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-observability
Source: https://github.com/civictechdc/votecatcher/tree/main/backend/.agent/skills/python-observability
Command: npx skills add https://github.com/civictechdc/votecatcher --skill python-observability-civictechdc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Instrument Python applications to achieve observable, diagnosable behavior and actionable insights.

Core Features & Use Cases

  • Structured logging: emit JSON logs with consistent fields to enable powerful queries.
  • Metrics & tracing: collect metrics (Prometheus) and traces (OpenTelemetry) across services.
  • Use Case: Add observability to a REST API to quickly identify latency bottlenecks and error hotspots.

Quick Start

Configure your Python service to emit structured logs, metrics, and traces in the shortest path to valuable insights.

Frequently Asked Questions about python-observability

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

FAQPage Schema
How do I add structured logging and distributed tracing to a Python application?▼

You add structured logging and distributed tracing by instrumenting your Python application with libraries like structlog and OpenTelemetry to emit JSON logs, collect Prometheus metrics, and generate traces with correlation IDs for production observability.

What is the quickest way to set up OpenTelemetry and metrics collection in Python?▼

The quickest path to set up OpenTelemetry and metrics collection involves configuring your Python service to emit structured logs, collect Prometheus metrics, and export traces using guided OpenTelemetry integration for immediate, actionable insights.

Can I use this observability setup for background tasks and microservices, not just REST APIs?▼

Yes, this observability setup applies across web services, background tasks, and microservices, allowing you to instrument distributed Python components to identify latency bottlenecks and error hotspots consistently.

What's the best way to debug latency bottlenecks and error hotspots in Python microservices?▼

The best way to debug latency bottlenecks is by implementing distributed tracing and structured logging with correlation IDs, enabling you to query consistent JSON log fields and trace requests across Python microservices.

Do I need correlation IDs to correlate logs and traces across Python services?▼

Yes, you need correlation IDs to link structured logs and distributed traces across Python services, ensuring that error handling and performance tuning data can be traced accurately across microservices.

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