How does this skill handle reactive programming?

It includes patterns for Reactive Context Propagation, ensuring MDC data and trace IDs are preserved across Project Reactor threads using doFinally blocks to prevent context leakage.

What logging standards does it follow?

It enforces the use of SLF4J placeholders for performance and LogstashEncoder for production-ready JSON logging while strictly prohibiting the logging of PII.

Does it support Kubernetes?

Yes, it provides specific configurations for liveness and readiness probes via Spring Boot Actuator, compatible with Kubernetes health check requirements.

Which metrics tools are supported?

The skill focuses on Micrometer for vendor-neutral instrumentation, specifically configured to export data via Prometheus endpoints for Grafana visualization.

Can I customize alerting thresholds?

Yes, the skill provides a predefined matrix of latency, error rate, and resource saturation thresholds that can be used as a template for your specific SLA requirements.

Spring Boot Observability Patterns

Name: Spring Boot Observability Patterns
Author: taipt1504

bytaipt1504

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分析与监控

Implements comprehensive logging, metrics, and distributed tracing patterns for production-grade Java Spring Boot applications.

This skill provides a standardized framework for observability in Spring Boot environments, focusing on structured JSON logging, distributed tracing, and metrics instrumentation. It automates the configuration of Logstash encoders for ELK/Loki pipelines, distributed tracing via Micrometer and OTLP, and custom application metrics for Prometheus and Grafana. Designed for both traditional and reactive stacks, it ensures consistent MDC context propagation, Kubernetes-compatible health probes, and robust alerting thresholds while enforcing security best practices like PII masking and placeholder-based logging.

主要功能

01Structured JSON logging with Logstash encoder and MDC correlation

02Distributed tracing setup using Micrometer Tracing and OTLP endpoints

03Custom metric instrumentation with @Timed, @Counted, and MeterRegistry

04Kubernetes-ready liveness and readiness health indicators

05Reactive context propagation for Project Reactor and WebFlux

063 GitHub stars

使用场景

01Configuring production-ready monitoring for new microservices

02Debugging distributed transactions using trace propagation and span tagging

03Establishing alerting thresholds for P99 latency and error rates in Grafana

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add taipt1504/agent-skills observability-patterns

For use in Claude.ai and ChatGPT

主要功能

01Structured JSON logging with Logstash encoder and MDC correlation

02Distributed tracing setup using Micrometer Tracing and OTLP endpoints

03Custom metric instrumentation with @Timed, @Counted, and MeterRegistry

04Kubernetes-ready liveness and readiness health indicators

05Reactive context propagation for Project Reactor and WebFlux

063 GitHub stars

使用场景

01Configuring production-ready monitoring for new microservices

02Debugging distributed transactions using trace propagation and span tagging

03Establishing alerting thresholds for P99 latency and error rates in Grafana

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add taipt1504/agent-skills observability-patterns

For use in Claude.ai and ChatGPT