Prometheus Analysis

The quick brown fox jumps over the lazy dog.

3.1k|368|Updated Feb 26, 2026
One-click install
npx skills add https://github.com/openocta/openocta --skill prometheus-analysis-openocta
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Prometheus Analysis
Source: https://github.com/openocta/openocta/tree/main/src/embed/skills/prometheus-analysis
Command: npx skills add https://github.com/openocta/openocta --skill prometheus-analysis-openocta

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you understand and query Prometheus metrics and alerts to diagnose system issues and perform root cause analysis.

Core Features & Use Cases

  • Querying Metrics: Write and execute PromQL queries to fetch specific time-series data.
  • Analyzing Alerts: Interpret Prometheus alerts to understand their context and impact.
  • Root Cause Analysis: Use metric patterns and correlations to identify the underlying causes of incidents.
  • Use Case: When an alert fires for high error rates, use this Skill to query the error rate over time, identify the specific endpoints or instances affected, and correlate it with latency or CPU usage to pinpoint the issue.

Quick Start

Use the Prometheus Analysis skill to find the error rate percentage for the last hour.

Frequently Asked Questions about Prometheus Analysis

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

FAQPage Schema
How do I use Prometheus metrics for root cause analysis?

Prometheus root cause analysis uses metric patterns and correlations to identify underlying causes of incidents. You query time-series data using PromQL to investigate metric anomalies and correlate them with alert data to pinpoint system issues.

How do I write PromQL queries to investigate metric anomalies?

Writing PromQL queries for metric anomalies involves fetching specific time-series data points to investigate deviations. You execute targeted queries to retrieve metric data over specific time ranges and interpret the resulting time-series patterns.

How do I interpret Prometheus alerts to understand their impact?

Interpreting Prometheus alerts requires analyzing the alert context and correlating it with active metric data. You query active alerts through the integration to understand the specific system conditions that triggered the alerting rules.

Can I query active alerts and metric data using a Prometheus MCP integration?

Querying active alerts and metric data is supported through the Prometheus MCP integration. This allows you to retrieve active alerts and fetch specific time-series data directly for analyzing system issues and performing root cause analysis.

What is the best way to correlate error rates with latency using Prometheus?

Correlating error rates with latency in Prometheus involves querying both metrics over time using PromQL. You fetch the error rate percentage and latency data, then analyze the time-series patterns to identify if increased latency is causing high error rates.

Why does querying time-series data patterns help with diagnosing system issues?

Querying time-series data patterns helps diagnose system issues by revealing metric anomalies and correlations over time. Analyzing these patterns allows you to identify the specific endpoints or instances affected and pinpoint the underlying causes of incidents.