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drewid74
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grafana-prometheus-monitoring

Provision Grafana dashboards, datasources, and Prometheus SLO alerts as code.

8|1|Updated Mar 30, 2026
Search Tags:#monitoring#infrastructure as code#grafana#prometheus#gpu metrics#slo alerts#ai inference monitoring
One-click install
npx skills add https://github.com/drewid74/ai_skills --skill grafana-prometheus-monitoring
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: grafana-prometheus-monitoring
Source: https://github.com/drewid74/ai_skills/tree/main/grafana-prometheus-monitoring
Command: npx skills add https://github.com/drewid74/ai_skills --skill grafana-prometheus-monitoring

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill solves the problem of deploying and operating reliable monitoring for Grafana and Prometheus without brittle, manual UI changes that waste tokens, drift over time, and hide real incidents.

Core Features & Use Cases

  • Provisioning-first observability: idempotent Grafana provisioning (datasources and dashboards) with allowUiUpdates: false, plus deployment via Grafana API, Grizzly, or Terraform.
  • Prometheus metrics + recording rules: scrape infrastructure/AI/GPU targets using file-based service discovery, then normalize and precompute SLIs/metrics using recording rules.
  • Operational alerting with guardrails: MWMBR burn-rate SLO alerts, Alertmanager routing (critical→PagerDuty, warning→Slack), runbook URL enforcement, and flapping/cardinality controls.
  • AI + GPU coverage: GPU metrics via DCGM Exporter/ROCm SMI exporter, inference metrics via LiteLLM/vLLM/Ollama endpoints, training metrics via Pushgateway, and vector DB monitoring via native metrics.

Quick Start

Use the grafana-prometheus-monitoring skill to generate a complete monitoring plan and Grafana provisioning configuration for your Docker Compose stack so dashboards, datasources, scrape targets, and SLO-based alerts are ready without manual UI steps.

Frequently Asked Questions about grafana-prometheus-monitoring

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

FAQPage Schema
How do I provision Grafana dashboards and datasources as code without manual UI changes?▼

You can provision Grafana dashboards and datasources as code using idempotent configurations with allowUiUpdates: false, or deploy them via the Grafana API, Grizzly, or Terraform to prevent configuration drift and eliminate manual UI steps.

How do I set up Prometheus monitoring for GPU and AI inference targets?▼

Set up Prometheus monitoring for AI and GPU targets by using file-based service discovery to scrape DCGM Exporter, ROCm SMI, and LiteLLM or vLLM endpoints, then normalize the metrics using recording rules to precompute SLIs.

Can I use this infrastructure as code monitoring setup in both Docker Compose and Kubernetes?▼

Yes, the monitoring setup supports both Docker Compose and Kubernetes environments, allowing you to apply provisioning configurations to infrastructure, AI inference, training, and vector database monitoring scenarios across different deployment platforms.

What is the best way to prevent configuration drift when managing Grafana and Prometheus alerting?▼

The best way to prevent drift is adopting a provisioning-first approach that deploys dashboards, datasources, scrape targets, and SLO-driven alerting from git, setting allowUiUpdates: false to block manual UI overrides.

How do I monitor AI training jobs and vector databases with Prometheus recording rules?▼

Monitor AI training jobs via Pushgateway metrics and vector databases via native exporter endpoints, then use Prometheus recording rules to precompute and normalize SLIs for consistent alerting and dashboard visualization.

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