Why is label cardinality important in code reviews?

High cardinality, such as using unique IDs or full URLs as labels, creates a massive number of time series that can crash your Prometheus server or significantly increase storage costs.

What common Go Prometheus mistakes does this skill catch?

It identifies high-cardinality labels, incorrect metric types (like using a Gauge instead of a Counter), and metrics registered inside request handlers which causes duplicate registration panics.

Can this help with histogram bucket configuration?

Yes, it provides guidelines and checklists for setting appropriate bucket boundaries for request durations and other distributions.

Does it cover the prometheus/client_golang library?

Yes, the skill is specifically tailored for Go applications using the official Prometheus Go client library and its patterns.

Prometheus Go Code Review

Name: Prometheus Go Code Review
Author: existential-birds

byexistential-birds

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Analytics & Monitoring

Validates Prometheus metrics implementation in Go applications to ensure optimal observability and performance.

About

This skill provides comprehensive code review guidance for developers implementing Prometheus instrumentation in Go. It helps identify common pitfalls such as high-cardinality labels that can degrade monitoring system performance, incorrect metric type selection, and improper registration patterns that lead to panics. By applying these industry-standard best practices and checklists, developers can ensure their Go services provide accurate, performant, and reliable telemetry data through the official prometheus/client_golang library.

Key Features

14 GitHub stars
Checks for correct /metrics endpoint exposure and collector thread-safety
Validates metric type selection including Counter, Gauge, and Histogram
Enforces Prometheus naming conventions and required unit suffixes
Identifies improper per-request metric registration that causes panics
Detects high-cardinality label anti-patterns like user IDs or request paths

Use Cases

Debugging high memory usage or series explosion in Prometheus scrapers
Refactoring legacy monitoring code to follow modern observability patterns
Performing automated code reviews on new Go service instrumentation

About

Key Features

14 GitHub stars
Checks for correct /metrics endpoint exposure and collector thread-safety
Validates metric type selection including Counter, Gauge, and Histogram
Enforces Prometheus naming conventions and required unit suffixes
Identifies improper per-request metric registration that causes panics
Detects high-cardinality label anti-patterns like user IDs or request paths

Use Cases

Debugging high memory usage or series explosion in Prometheus scrapers
Refactoring legacy monitoring code to follow modern observability patterns
Performing automated code reviews on new Go service instrumentation