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opentelemetry

Configure OpenTelemetry instrumentation for Python web frameworks and async apps.

6|2|Updated Jan 6, 2026
Search Tags:#python#observability#instrumentation#opentelemetry#distributed-tracing#propagation#exporters
One-click install
npx skills add https://github.com/acaprino/claude-code-daodan --skill opentelemetry-acaprino
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: opentelemetry
Source: https://github.com/acaprino/claude-code-daodan/tree/main/plugins/opentelemetry/skills/opentelemetry
Command: npx skills add https://github.com/acaprino/claude-code-daodan --skill opentelemetry-acaprino

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

OpenTelemetry Python instrumentation knowledge base provides a comprehensive guide to implementing distributed tracing, context propagation, and production observability patterns across Python services, with SDK v1.42.1 targeted guidance.

Core Features & Use Cases

  • Coverage of auto- and manual-instrumentation for popular Python frameworks (FastAPI, Django, Flask) and asynchronous code.
  • Guidance on propagators, sampling strategies, OTLP exporters, and production readiness patterns.
  • Real-world use cases including tracing, metrics, and log correlation across microservices.

Quick Start

Install the OpenTelemetry Python tooling and enable auto-instrumentation to begin collecting traces and metrics with minimal code.

Frequently Asked Questions about opentelemetry

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

FAQPage Schema
How do I add distributed tracing to a FastAPI or Flask application?▼

Distributed tracing for FastAPI or Flask is implemented through OpenTelemetry auto-instrumentation, enabling data collection with minimal code changes. The Skill provides setup patterns for popular Python frameworks to capture traces and metrics automatically.

What is the best way to configure context propagation across Python microservices?▼

OpenTelemetry context propagation uses propagators to transfer trace context across service boundaries in Python microservices. You configure the propagator on both outgoing and incoming requests to ensure trace spans link correctly across distributed systems.

Can I use OpenTelemetry to instrument asynchronous Python code and Celery tasks?▼

OpenTelemetry supports instrumenting asynchronous Python code and Celery tasks. The Skill covers manual and auto-instrumentation patterns for async frameworks, ensuring traces and context propagate correctly across background job executions.

How do OTLP exporters work for sending Python application traces to a backend?▼

OTLP exporters send OpenTelemetry traces, metrics, and logs from your Python application to an observability backend. You configure the exporter endpoint in the SDK to flush telemetry data periodically over HTTP or gRPC protocols.

When should I use manual instrumentation instead of auto-instrumentation in Python?▼

Manual instrumentation is necessary when auto-instrumentation does not capture custom business logic or specific code paths. You add custom spans to OpenTelemetry in Python to trace specific functions, database queries, or internal processes not covered by automatic libraries.

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