How does this skill help with PromQL?

It provides templates and logic for generating complex queries, including aggregations, rate calculations, and histogram percentiles.

Does it support different Prometheus metric types?

Yes, it includes specialized guidance for working with Counters, Gauges, Histograms, and Summaries.

Can it help diagnose firing alerts?

Yes, it analyzes alert expressions, thresholds, and durations to provide context and suggest specific investigation steps.

Can I use this for historical trend analysis?

Absolutely. It provides guidance on using range queries and offset modifiers to compare current metrics with historical baselines.

Prometheus Analysis

Name: Prometheus Analysis
Author: evangelosmeklis

byevangelosmeklis

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Analytics & Monitoring

Analyzes Prometheus metrics and alerts to identify root causes of production issues using PromQL and time-series data interpretation.

About

The Prometheus Analysis skill transforms Claude into an observability expert capable of querying and interpreting complex time-series data. It provides specialized guidance for writing efficient PromQL queries, diagnosing firing alerts, and recognizing critical system patterns such as resource exhaustion, memory leaks, and traffic spikes. By correlating metrics like latency, error rates, and CPU usage, this skill helps developers and SREs perform rapid root cause analysis (RCA) and maintain system reliability in production environments.

Key Features

Pattern recognition for spikes, gradual increases, and service drops
4 GitHub stars
Standardized troubleshooting workflows for production incidents
Expert PromQL query generation for instant and range queries
Multi-metric correlation for comprehensive root cause analysis
Automated interpretation of firing alerts and their thresholds

Use Cases

Investigating the root cause of high error rate alerts in microservices
Calculating 95th percentile latency to identify performance bottlenecks
Correlating traffic surges with resource utilization during deployments

About

Key Features

Pattern recognition for spikes, gradual increases, and service drops
4 GitHub stars
Standardized troubleshooting workflows for production incidents
Expert PromQL query generation for instant and range queries
Multi-metric correlation for comprehensive root cause analysis
Automated interpretation of firing alerts and their thresholds

Use Cases

Investigating the root cause of high error rate alerts in microservices
Calculating 95th percentile latency to identify performance bottlenecks
Correlating traffic surges with resource utilization during deployments