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python-observability

Instrument Python applications with JSON logs, Prometheus metrics, and OpenTelemetry tracing.

6|Updated Feb 25, 2026
Search Tags:#logging#python#observability#tracing#prometheus#opentelemetry#structlog
One-click install
npx skills add https://github.com/archibate/archibate-skills --skill python-observability-archibate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-observability
Source: https://github.com/archibate/archibate-skills/tree/main/old-skills/redundant-skills/python-observability
Command: npx skills add https://github.com/archibate/archibate-skills --skill python-observability-archibate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Production Python applications often lack consistent, machine-readable logs, reliable metrics, and end-to-end traces, making it difficult to answer what happened, where it happened, and why without deploying new code or running time-consuming investigations.

Core Features & Use Cases

  • Structured Logging: Emit JSON logs with consistent fields for filtering, searching, and ease of ingestion into log systems.
  • Metrics with Prometheus: Collect bounded-cardinality counters, histograms, and gauges to track the four golden signals and drive alerts.
  • Distributed Tracing: Instrument code with OpenTelemetry spans and propagate correlation IDs across services for end-to-end request visibility.
  • Use Case: Instrument a FastAPI or background worker service to add JSON logs, Prometheus metrics, and OTLP tracing so that on-call engineers can rapidly triage latency spikes and error cascades.

Quick Start

Instrument your FastAPI application with JSON-structured logs, Prometheus metrics, and OpenTelemetry tracing by adding the provided logging configuration, a correlation-ID middleware, and metric decorators to your endpoints.

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 Prometheus metrics to a Python service?▼

To add structured logging and Prometheus metrics to a Python service, instrument the application with JSON log configurations, bounded-cardinality counters, and metric decorators to surface actionable telemetry for incidents.

What is the best way to implement distributed tracing in Python microservices?▼

The best way to implement distributed tracing in Python microservices is using OpenTelemetry spans and correlation ID middleware to propagate context across services for end-to-end request visibility.

How does correlation ID propagation work for debugging production issues?▼

Correlation ID propagation works by attaching a unique identifier to requests and passing it across services via OpenTelemetry spans, allowing on-call engineers to track end-to-end request visibility and triage error cascades.

Why do my Prometheus metrics cause high cardinality in Python applications?▼

Prometheus metrics cause high cardinality in Python applications when labels attach to unbounded values like user IDs, which this instrumentation avoids by strictly collecting bounded-cardinality counters, histograms, and gauges.

Do I need OpenTelemetry to instrument a Python background worker for observability?▼

You need OpenTelemetry to instrument a Python background worker for full observability, as it provides the spans required to propagate correlation IDs and trace execution paths alongside structured JSON logs and metrics.

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