When should I use a Histogram vs. a Summary in Prometheus?

Use Histograms as the default for latencies and distributions because they can be aggregated across multiple instances. Use Summaries only when you need exact quantiles from a single instance and know the quantiles at instrumentation time.

Does it support alerting and recording rule design?

Absolutely. It focuses on 'symptom-based' alerting to reduce noise and provides naming conventions for recording rules to pre-compute expensive dashboard queries.

Can this skill help me write PromQL queries?

Yes, it assists in crafting correct PromQL for rate calculations, histogram quantiles, and complex aggregations while ensuring you follow rules like 'rate first, then aggregate'.

How does this skill help with label cardinality?

It provides guidance on keeping label sets manageable (typically <10 per metric) and identifies 'unbounded' values like user IDs or full URLs that can crash a Prometheus server.

Prometheus Observability Expert

Name: Prometheus Observability Expert
Author: xobotyi

byxobotyi

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Analytics & Monitoring

Implements high-quality Prometheus instrumentation, metrics naming, and PromQL queries following industry best practices.

This skill provides Claude with deep expertise in the Prometheus monitoring ecosystem, enabling it to assist with application instrumentation, metric selection, and label cardinality management. It guides the creation of standardized naming conventions, efficient PromQL queries, and robust alerting rules while avoiding common pitfalls like high cardinality or statistically invalid aggregations. Whether you are instrumenting a backend service, building custom exporters, or designing observability dashboards, this skill ensures your monitoring data is accurate, scalable, and actionable.

Key Features

01PromQL Optimization: Assists in writing and debugging complex queries, including rate calculations and histogram quantiles.

024 GitHub stars

03Standardized Naming: Enforces industry-standard naming patterns and unit conventions for clear observability.

04Cardinality Management: Identifies and prevents high-cardinality label patterns that impact performance.

05Metric Type Selection: Expert guidance on choosing between Counters, Gauges, Histograms, and Summaries.

06Alerting & Recording Rules: Designing symptom-based alerts and efficient pre-computed recording rules.

Use Cases

01Instrumenting backend services with application-specific metrics for better visibility.

02Designing symptom-based alerting rules to detect service degradation and errors.

03Optimizing existing Prometheus queries for better performance and statistical accuracy.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add xobotyi/cc-foundry prometheus

For use in Claude.ai and ChatGPT

Download Skill