Does this work with standard OTLP exporters?

Yes, it provides configuration patterns for OTLP exporters, making it compatible with observability platforms like Jaeger, Honeycomb, and New Relic.

Can I use this to correlate logs with my traces?

Absolutely. The skill demonstrates how to extract the current span context to inject trace IDs into your logging output for easier troubleshooting.

Which Python frameworks are supported by this skill?

The skill provides specialized patterns for FastAPI and Flask, but the core OpenTelemetry principles can be applied to any Python backend service.

Does it support manual span creation for business logic?

Yes, it includes specific code patterns for defining custom spans to measure the performance of internal functions and database queries.

How does it handle communication between microservices?

It includes implementation guides for context propagation using the Requests library, ensuring trace continuity as requests move between downstream services.

Python OpenTelemetry Instrumentation

Name: Python OpenTelemetry Instrumentation
Author: Agentient

byAgentient

0•

Analytics & Monitoring

Instruments Python backend services with OpenTelemetry for comprehensive distributed tracing and performance monitoring.

This skill enables Claude to seamlessly integrate OpenTelemetry into Python-based backend architectures, specifically targeting frameworks like FastAPI and Flask. It provides automated solutions for setting up tracers, configuring OTLP exporters, and implementing auto-instrumentation for popular libraries like Requests. Whether you are debugging microservice latency, correlating logs with traces, or managing manual spans for complex business logic, this skill offers the implementation patterns and best practices needed to maintain high-quality observability across your Python stack.

Key Features

01Context propagation for distributed tracing across services

02Trace ID log correlation for simplified debugging

03Auto-instrumentation for FastAPI and Flask applications

04Manual span creation for granular business logic tracing

05OTLP exporter configuration for backend integration

060 GitHub stars

Use Cases

01Identifying performance bottlenecks in distributed microservices

02Correlating application logs with specific request traces

03Implementing standardized observability in new Python backend projects

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add agentient/vibekit python-backend-opentelemetry-instrumentation

For use in Claude.ai and ChatGPT

Download Skill