python-observability

Name: python-observability
Availability: InStock
Author: jhabbig

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

Updated Apr 5, 2026

Search Tags:#python #metrics #observability #tracing #prometheus #opentelemetry #structured logging

Ready to run

npx skills add https://github.com/Jhabbig/Habbig --skill python-observability-jhabbig

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-observability
Source: https://github.com/Jhabbig/Habbig/tree/main/.claude/plugins/wshobson/python-development/skills/python-observability
Command: npx skills add https://github.com/Jhabbig/Habbig --skill python-observability-jhabbig

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you understand what is happening inside Python applications by adding structured logs, meaningful metrics, and distributed traces, so production issues can be diagnosed without guesswork.

Core Features & Use Cases

Structured Logging: Emit consistent, machine-readable logs that are easy to search, filter, and correlate across services.
Metrics and Alerting: Track latency, traffic, errors, and saturation with Prometheus-style metrics to spot regressions early.
Distributed Tracing: Follow a request across service boundaries with correlation IDs and spans to find where failures originate.
Use Case: If a FastAPI endpoint is slowing down in production, this Skill helps you add request timing, propagate trace context, and capture useful error details for root-cause analysis.

Quick Start

Ask for a Python observability setup that adds structured logging, request metrics, correlation IDs, and distributed tracing to your service.

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 service?▼

To add structured logging and distributed tracing to a Python service, instrument your code with consistent log fields, correlation ID propagation, and OpenTelemetry-compatible spans to capture machine-readable data for request correlation across APIs and background jobs.

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

Monitoring Python application metrics with Prometheus involves tracking latency, traffic, errors, and saturation using bounded metric labels. This approach spots regressions early by emitting reliable, Prometheus-style metrics for your production workloads.

Can I use OpenTelemetry to trace requests across FastAPI endpoints and background jobs?▼

Yes, you can use OpenTelemetry to trace requests across FastAPI endpoints and background jobs. The instrumentation propagates trace context and correlation IDs across service boundaries to help you follow requests and find where failures originate.

How do I propagate correlation IDs for production debugging in Python?▼

Propagating correlation IDs for production debugging in Python requires consistent log fields and context propagation across service boundaries. This allows you to follow a single request across distributed services to quickly diagnose failures and latency bottlenecks.

Do I need bounded metric labels for Python observability?▼

Yes, you need bounded metric labels for Python observability to prevent cardinality explosions in your monitoring backend. Bounding labels ensures your Prometheus metrics remain reliable, queryable, and cost-effective as your application scales.