observability-integrator

Name: observability-integrator
Availability: InStock
Author: JetBrains

Instrument Kotlin and Spring services with metrics, logs, and traces.

302|22|Updated Feb 12, 2026

Search Tags:#logs #metrics #observability #tracing #instrumentation #kotlin #spring

Ready to run

npx skills add https://github.com/JetBrains/skills --skill observability-integrator-jetbrains

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: observability-integrator
Source: https://github.com/JetBrains/skills/tree/main/observability-integrator
Command: npx skills add https://github.com/JetBrains/skills --skill observability-integrator-jetbrains

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Kotlin and Spring services often lack end-to-end observability, making incident diagnosis slow and recovery uncertain. This skill provides a structured approach to instrumenting services so operators can understand health, performance, and dependencies.

Core Features & Use Cases

Design and implement metrics, logs, and traces that answer operational questions.
Define SLO-driven metrics and alerting strategies for Kotlin-based microservices and their downstream dependencies.
Use cases include diagnosing production incidents, optimizing telemetry budgets, and ensuring async/coroutine flows remain observable in production environments.

Quick Start

Define core service journeys, then add correlated metrics, traces, and health endpoints to enable end-to-end observability.

Frequently Asked Questions about observability-integrator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I instrument Kotlin and Spring services for end-to-end observability?▼

Instrument Kotlin and Spring services for end-to-end observability by defining core service journeys, then adding correlated metrics, traces, and health endpoints to understand service behavior and dependencies during normal and failure conditions.

What is the best way to maintain tracing across async boundaries in Kotlin coroutines?▼

Maintaining tracing across async boundaries in Kotlin coroutines requires specific instrumentation patterns that propagate context, ensuring async flows remain observable in production environments for accurate incident diagnosis.

How do I design SLO-driven metrics and alerting for Kotlin microservices?▼

Design SLO-driven metrics and alerting for Kotlin microservices by specifying instrumentation patterns and cardinality controls that align telemetry with operational questions and downstream dependencies to meet service level objectives.

How do I control metric cardinality when adding observability to Spring services?▼

Control metric cardinality when adding observability to Spring services by specifying instrumentation patterns that limit high-dimensional labels, optimizing telemetry budgets while ensuring metrics remain actionable for alerting readiness.

Does this observability approach work for diagnosing production incidents in microservices?▼

Yes, this observability approach works for diagnosing production incidents in microservices by providing structured metrics, logs, and traces that answer operational questions and make service behavior transparent under failure conditions.