Can I use this with Prometheus and Jaeger?

Absolutely. The skill includes specific implementation patterns for OTLP, Jaeger, and Prometheus exporters to ensure your data reaches your preferred monitoring backend.

Which Python frameworks are supported?

The skill covers major frameworks including Flask, FastAPI, and Django, as well as database layers like SQLAlchemy and HTTP clients like Requests.

Does this skill support automatic instrumentation?

Yes, it provides guidance on using the opentelemetry-instrument tool and bootstrap commands to automatically instrument common libraries like Flask, FastAPI, and Requests without manual code changes.

Does it support the latest OpenTelemetry signals?

Yes, it provides stable implementation patterns for all three primary observability signals: Traces, Metrics, and Logs.

How does it handle context propagation?

It implements standard W3C Trace Context and Baggage propagators, allowing trace IDs and metadata to flow seamlessly across service boundaries in a distributed system.

Python OpenTelemetry Observability

Name: Python OpenTelemetry Observability
Author: zach-source

byzach-source

0•

分析と監視

Implements comprehensive observability in Python applications using OpenTelemetry for distributed tracing, metrics collection, and logging.

The Python OpenTelemetry skill empowers developers to integrate industry-standard observability into their Python distributed systems. It provides deep expertise in setting up the OTel SDK, configuring automatic instrumentation for frameworks like Flask and FastAPI, and implementing manual tracing for granular business logic. By leveraging best practices for span attributes, semantic conventions, and various exporters like OTLP and Prometheus, this skill ensures robust monitoring, faster debugging, and clear visibility into complex microservices architectures.

主な機能

01Real-time metrics collection including counters, histograms, and gauges

02Multi-exporter support for OTLP, Jaeger, Prometheus, and Console

030 GitHub stars

04Implementation of OTel semantic conventions and production best practices

05Automatic instrumentation for Flask, FastAPI, Requests, and SQLAlchemy

06Distributed tracing with manual span creation and context propagation

ユースケース

01Visualizing end-to-end request flows across distributed Python services

02Debugging performance bottlenecks and latency in microservices

03Monitoring system health and resource usage via Prometheus and OTLP

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zach-source/claude-plugins otel-python

For use in Claude.ai and ChatGPT

Download Skill