grafana-dashboards
Lets your agent create visual dashboards to monitor system performance in real-time. It's useful for having a clear view of how applications are running.
What it does
This tool helps create and manage visual dashboards in Grafana. They are used to display important real-time data from your system or application, such as performance or error counts.
When to use it
- ✓When you need to see important system metrics.
- ✓When creating charts to understand application performance.
- ✓To monitor infrastructure status or business indicators.
Example of what to ask your AI
“Create a dashboard to monitor my API's request rate and errors.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
You describe what data you want to see and how it should be presented (charts, numbers, tables). The tool then generates the necessary configuration for Grafana to display this information.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add wshobson/agents --skill grafana-dashboardsOr download just the file:
curl -L https://raw.githubusercontent.com/wshobson/agents/main/plugins/observability-monitoring/skills/grafana-dashboards/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Grafana Dashboards
Create and manage production-ready Grafana dashboards for comprehensive system observability.
Purpose
Design effective Grafana dashboards for monitoring applications, infrastructure, and business metrics.
When to Use
- Visualize Prometheus metrics
- Create custom dashboards
- Implement SLO dashboards
- Monitor infrastructure
- Track business KPIs
Dashboard Design Principles
1. Hierarchy of Information
┌─────────────────────────────────────┐
│ Critical Metrics (Big Numbers) │
├─────────────────────────────────────┤
│ Key Trends (Time Series) │
├─────────────────────────────────────┤
│ Detailed Metrics (Tables/Heatmaps) │
└─────────────────────────────────────┘
2. RED Method (Services)
- Rate - Requests per second
- Errors - Error rate
- Duration - Latency/response time
3. USE Method (Resources)
- Utilization - % time resource is busy
- Saturation - Queue length/wait time
- Errors - Error count
Dashboard Structure
API Monitoring Dashboard
{
"dashboard": {
"title": "API Monitoring",
"tags": ["api", "production"],
"timezone": "browser",
"refresh": "30s",
"panels": [
{
"title": "Request Rate",
"type": "graph",
"targets": [
{
"expr": "sum(rate(http_requests_total[5m])) by (service)",
"legendFormat": "{{service}}"
}
],
"gridPos": { "x": 0, "y": 0, "w": 12, "h": 8 }
},
{
"title": "Error Rate %",
"type": "graph",
"targets": [
{
"expr": "(sum(rate(http_requests_total{status=~\"5..\"}[5m])) / sum(rate(http_requests_total[5m]))) * 100",
"legendFormat": "Error Rate"
}
],
"alert": {
"conditions": [
{
"evaluator": { "params": [5], "type": "gt" },
"operator": { "type": "and" },
"query": { "params": ["A", "5m", "now"] },
"type": "query"
}
]
},
"gridPos": { "x": 12, "y": 0, "w": 12, "h": 8 }
},
{
"title": "P95 Latency",
"type": "graph",
"targets": [
{
"expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))",
"legendFormat": "{{service}}"
}
],
"gridPos": { "x": 0, "y": 8, "w": 24, "h": 8 }
}
]
}
}
Panel Types
1. Stat Panel (Single Value)
{
"type": "stat",
"title": "Total Requests",
"targets": [
{
"expr": "sum(http_requests_total)"
}
],
"options": {
"reduceOptions": {
"values": false,
"calcs": ["lastNotNull"]
},
"orientation": "auto",
"textMode": "auto",
"colorMode": "value"
},
"fieldConfig": {
"defaults": {
"thresholds": {
"mode": "absolute",
"steps": [
{ "value": 0, "color": "green" },
{ "value": 80, "color": "yellow" },
{ "value": 90, "color": "red" }
]
}
}
}
}
2. Time Series Graph
{
"type": "graph",
"title": "CPU Usage",
"targets": [
{
"expr": "100 - (avg by (instance) (rate(node_cpu_seconds_total{mode=\"idle\"}[5m])) * 100)"
}
],
"yaxes": [
{ "format": "percent", "max": 100, "min": 0 },
{ "format": "short" }
]
}
3. Table Panel
{
"type": "table",
"title": "Service Status",
"targets": [
{
"expr": "up",
"format": "table",
"instant": true
}
],
"transformations": [
{
"id": "organize",
"options": {
"excludeByName": { "Time": true },
"indexByName": {},
"renameByName": {
"instance": "Instance",
"job": "Service",
"Value": "Status"
}
}
}
]
}
4. Heatmap
{
"type": "heatmap",
"title": "Latency Heatmap",
"targets": [
{
"expr": "sum(rate(http_request_duration_seconds_bucket[5m])) by (le)",
"format": "heatmap"
}
],
"dataFormat": "tsbuckets",
"yAxis": {
"format": "s"
}
}
Variables
Query Variables
{
"templating": {
"list": [
{
"name": "namespace",
"type": "query",
"datasource": "Prometheus",
"query": "label_values(kube_pod_info, namespace)",
"refresh": 1,
"multi": false
…
FAQ
What are 'metrics'?
Metrics are numerical data that describe the behavior of a system or application, such as access counts, response times, or memory usage.
What is Grafana?
Grafana is a popular program that allows you to create visual dashboards to monitor data from various sources.
What are 'dashboards'?
Dashboards are collections of charts and visualizations that display important information at a glance, helping to quickly understand a system's status.
Details
Original description by the author
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
repo: wshobson/agents
Popularity
- GitHub stars
- 41k
- Ranking position
- #4908
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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