prometheus

Enforce Prometheus metric naming, types, labels, and PromQL query best practices.

19|Updated Jan 30, 2026
One-click install
npx skills add https://github.com/xobotyi/cc-foundry --skill prometheus-xobotyi
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: prometheus
Source: https://github.com/xobotyi/cc-foundry/tree/main/plugins/backend/skills/prometheus
Command: npx skills add https://github.com/xobotyi/cc-foundry --skill prometheus-xobotyi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you correctly instrument applications with Prometheus metrics, write effective PromQL queries, and define robust alerting and recording rules, preventing common pitfalls and ensuring reliable monitoring.

Core Features & Use Cases

  • Metric Design: Guides you on choosing the right metric types (Counter, Gauge, Histogram, Summary), naming conventions, and label strategies to avoid cardinality issues and ensure clarity.
  • PromQL Mastery: Provides rules and examples for writing efficient and accurate PromQL queries, including rate calculations, aggregations, and histogram usage.
  • Rule Definition: Offers best practices for creating alerting and recording rules, focusing on symptom-based alerting and correct aggregation patterns.
  • Use Case: When instrumenting a new microservice, use this Skill to ensure your metrics are named correctly, use appropriate types, and that your dashboards and alerts are built on sound PromQL queries.

Quick Start

Review the Prometheus naming conventions before instrumenting a new metric.

Frequently Asked Questions about prometheus

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

FAQPage Schema
How do I choose the right Prometheus metric types when instrumenting a new microservice?

Choosing the right Prometheus metric types involves matching data characteristics to Counter, Gauge, Histogram, or Summary. This Skill guides you through selecting appropriate types, naming conventions, and label strategies to avoid cardinality issues and ensure metric clarity.

What are the best practices for writing efficient PromQL queries for dashboards?

Writing efficient PromQL queries requires following rules for rate calculations, aggregations, and histogram usage. This Skill provides examples and enforces best practices for constructing accurate queries to monitor application performance and system health effectively.

How do I create effective Prometheus alerting and recording rules?

Creating effective Prometheus alerting and recording rules requires focusing on symptom-based alerting and correct aggregation patterns. This Skill offers best practices to define robust rules that prevent common monitoring pitfalls and ensure reliable alerts.

Why do my Prometheus metrics have cardinality issues and how can I avoid them?

Prometheus cardinality issues typically arise from improper label strategies and naming conventions. This Skill enforces best practices for metric design, guiding you on appropriate label usage to prevent cardinality explosions and maintain query performance.

When should I use a Histogram versus a Summary metric in Prometheus?

Using a Histogram versus a Summary metric in Prometheus depends on your aggregation needs and query requirements. This Skill guides you on choosing appropriate metric types and naming conventions to ensure clarity and maintainability for your specific monitoring use case.