Is there a performance overhead?

The setup uses the OpenTelemetry BatchSpanProcessor and non-blocking logging to minimize the impact on application request-response cycles.

Which observability backends are supported?

The skill supports any OTLP-compatible backend, with specific configuration examples provided for Vercel, Baselime, Axiom, and HyperDX.

How does the structured logger work?

It provides a custom logger utility that wraps console methods, formatting output as JSON and automatically attaching current OpenTelemetry trace and span IDs.

Can I measure specific function performance?

Yes, the skill includes 'withSpan' and 'measureAsync' helpers to wrap custom logic and record execution time and success/failure status.

Does this require specific Next.js versions?

It is optimized for Next.js 13, 14, and 15+, leveraging the native instrumentation.ts file for automatic server-side tracing.

Next.js Observability & Tracing

Name: Next.js Observability & Tracing
Author: mattwoodco

bymattwoodco

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分析与监控

Sets up OpenTelemetry-powered tracing and structured logging to monitor Next.js application performance and system health.

This skill automates the complex process of instrumenting Next.js applications with OpenTelemetry (OTEL) for deep visibility into server-side operations. It generates a standardized instrumentation setup that automatically tracks HTTP requests, fetch calls, and Node.js internals while providing a trace-aware structured logger. By integrating with major observability backends like Vercel, Axiom, and HyperDX, it ensures developers can debug production bottlenecks, correlate logs with traces, and maintain high-quality system health with minimal manual configuration.

主要功能

01Custom span and performance measurement helpers for granular code profiling and timing.

02Automatic OpenTelemetry SDK initialization via Next.js native instrumentation hooks.

03Out-of-the-box support for multiple backends including Vercel, Baselime, Axiom, and HyperDX.

04Structured JSON logging utilities that automatically inject trace and span IDs for correlation.

053 GitHub stars

06Environment-aware configuration to toggle telemetry and log levels between dev and production.

使用场景

01Debugging high-latency API routes by visualizing request traces across the execution stack.

02Monitoring production error rates with rich stack traces and contextual metadata in external dashboards.

03Centralizing logs with unique correlation IDs to track specific user journeys through server-side logic.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mattwoodco/skills observability

For use in Claude.ai and ChatGPT