mimir-prometheus-troubleshoot

Optimize Prometheus queries for metric analysis in Mimir environments.

1|Updated Dec 4, 2025

Search Tags:#monitoring #troubleshooting #metrics #observability #Prometheus #PromQL #Mimir

Ready to run

npx skills add https://github.com/timbuchinger/loadout --skill mimir-prometheus-troubleshoot

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mimir-prometheus-troubleshoot
Source: https://github.com/timbuchinger/loadout/tree/main/skills/mimir-prometheus-troubleshoot
Command: npx skills add https://github.com/timbuchinger/loadout --skill mimir-prometheus-troubleshoot

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you craft efficient PromQL queries and troubleshoot metric issues in Mimir and Prometheus, preventing high-cardinality problems and ensuring accurate, performant monitoring. It eliminates guesswork in metric analysis.

Core Features & Use Cases

Efficient PromQL Query Building: Guides in constructing precise queries for error rates, latency percentiles, and other critical metrics.
Metric Troubleshooting: Helps diagnose missing data, gaps, and other issues in your Mimir/Prometheus setup.
Cardinality Optimization: Provides best practices to avoid high-cardinality labels that can degrade performance.
Use Case: You need to calculate the 95th percentile latency for your 'frontend' service but are unsure how to write an efficient PromQL query. This Skill guides you to use histogram_quantile with appropriate aggregations and time ranges.

Quick Start

Craft a PromQL query to show the error rate for the 'payments' service in production over the last 5 minutes.

Frequently Asked Questions about mimir-prometheus-troubleshoot

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

FAQPage Schema

How do I write an efficient PromQL query to calculate error rates and latency percentiles?▼

PromQL queries for error rates use rate() functions on counter metrics, while latency percentiles use histogram_quantile() with appropriate time ranges and aggregations. This Skill guides you through constructing these queries with proper syntax and optimization to avoid performance issues in Mimir and Prometheus environments.

What causes missing data and gaps in my Prometheus metrics, and how do I troubleshoot them?▼

Missing metric data typically stems from cardinality issues, scrape failures, or incorrect label combinations. This Skill helps diagnose these problems by teaching metric debugging techniques, identifying gaps in your data, and applying best-practice query patterns to verify data presence across time ranges.

How do I reduce cardinality in Prometheus and Mimir to improve query performance?▼

High-cardinality labels degrade performance by creating too many unique metric combinations. This Skill provides cardinality optimization strategies, including label design best practices and query construction patterns that minimize metric explosion while maintaining observability.

Can I use PromQL aggregation strategies like sum by and histogram_quantile across different time ranges?▼

Yes, PromQL aggregation functions work across configurable time ranges. This Skill teaches how to apply sum by for dimensional analysis, use histogram_quantile for percentile calculations, and adjust time windows to balance accuracy with performance in your monitoring setup.

What are best practices for creating Prometheus recording rules to optimize query performance?▼

Recording rules pre-compute frequent queries and store results as new metrics, reducing runtime query load. This Skill covers recording rule creation, aggregation strategy selection, and optimization patterns aligned with Prometheus concepts to improve dashboard and alert performance.