grafana-dashboard logo

grafana-dashboard

grafana dashboard

aj-geddes/useful-ai-prompts385installs245stars

SKILL.md

Full skill instructions

Grafana Dashboard

Table of Contents

Overview

Design and implement comprehensive Grafana dashboards with multiple visualization types, variables, and drill-down capabilities for operational monitoring.

When to Use

  • Creating monitoring dashboards
  • Building operational insights
  • Visualizing time-series data
  • Creating drill-down dashboards
  • Sharing metrics with stakeholders

Quick Start

Minimal working example:

{
  "dashboard": {
    "title": "Application Performance",
    "description": "Real-time application metrics",
    "tags": ["production", "performance"],
    "timezone": "UTC",
    "refresh": "30s",
    "templating": {
      "list": [
        {
          "name": "datasource",
          "type": "datasource",
          "datasource": "prometheus"
        },
        {
          "name": "service",
          "type": "query",
          "datasource": "prometheus",
          "query": "label_values(requests_total, service)"
        }
      ]
    },
    "panels": [
      {
        "id": 1,
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

GuideContents
Grafana Dashboard JSONGrafana Dashboard JSON
Grafana Provisioning ConfigurationGrafana Provisioning Configuration
Grafana Alert ConfigurationGrafana Alert Configuration
Grafana API ClientGrafana API Client
Docker Compose SetupDocker Compose Setup

Best Practices

✅ DO

  • Use meaningful dashboard titles
  • Add documentation panels
  • Implement row-based organization
  • Use variables for flexibility
  • Set appropriate refresh intervals
  • Include runbook links in alerts
  • Test alerts before deploying
  • Use consistent color schemes
  • Version control dashboard JSON

❌ DON'T

  • Overload dashboards with too many panels
  • Mix different time ranges without justification
  • Create without runbooks
  • Ignore alert noise
  • Use inconsistent metric naming
  • Set refresh too frequently
  • Forget to configure datasources
  • Leave default passwords