Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
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name: python-observability
description: Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
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# Python Observability
Instrument Python applications with structured logs, metrics, and traces. When something breaks in production, you need to answer "what, where, and why" without deploying new code.
## When to Use This Skill
- Adding structured logging to applications
- Implementing metrics collection with Prometheus
- Setting up distributed tracing across services
- Propagating correlation IDs through request chains
- Debugging production issues
- Building observability dashboards
## Core Concepts
### 1. Structured Logging
Emit logs as JSON with consistent fields for production environments. Machine-readable logs enable powerful queries and alerts. For local development, consider human-readable formats.
### 2. The Four Golden Signals… load the full skill through Skill Me