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

分析と監視

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

概要

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.

主な機能

Context propagation for distributed tracing across services
Trace ID log correlation for simplified debugging
Auto-instrumentation for FastAPI and Flask applications
Manual span creation for granular business logic tracing
OTLP exporter configuration for backend integration
0 GitHub stars

ユースケース

Identifying performance bottlenecks in distributed microservices
Correlating application logs with specific request traces
Implementing 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

GitHub

概要