Adding a Metric

What problem does it solve?

This Skill helps engineering teams create or modify custom metrics for microservices to gain visibility into runtime behavior and performance, reducing guesswork and enabling data-driven decisions.

Core Features & Use Cases

• Metric definition & boilerplate generation: Define a metric in the service.yaml and generate the related boilerplate code.
• Runtime instrumentation: Record metric values from service logic to monitor throughput, latency, and error rates.
• Use Case: When you need to track a customer operation, like processing batches or cache hits, across a microservice to understand bottlenecks and reliability.

Quick Start

Define a new metric in your microservice's service.yaml, run go generate to produce the boilerplate, implement the setter (e.g., AddMyMetric) and optional callback, then observe metrics in your monitoring stack.