grafana-dashboard
About
This Claude Skill generates professional Grafana dashboards with visualizations, templating, and alerts. It's designed for developers building monitoring dashboards, creating data visualizations, or setting up operational insights. The skill provides JSON templates for comprehensive dashboards with multiple visualization types and drill-down capabilities.
Quick Install
Claude Code
Recommended/plugin add https://github.com/aj-geddes/useful-ai-promptsgit clone https://github.com/aj-geddes/useful-ai-prompts.git ~/.claude/skills/grafana-dashboardCopy and paste this command in Claude Code to install this skill
Documentation
Grafana Dashboard
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
Instructions
1. Grafana Dashboard JSON
{
"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,
"title": "Request Rate",
"type": "graph",
"gridPos": {"x": 0, "y": 0, "w": 12, "h": 8},
"targets": [
{
"expr": "sum(rate(requests_total{service=\"$service\"}[5m]))",
"legendFormat": "{{ method }}"
}
],
"yaxes": [
{
"format": "rps",
"label": "Requests per Second"
}
]
},
{
"id": 2,
"title": "Error Rate",
"type": "graph",
"gridPos": {"x": 12, "y": 0, "w": 12, "h": 8},
"targets": [
{
"expr": "sum(rate(requests_total{status_code=~\"5..\",service=\"$service\"}[5m])) / sum(rate(requests_total{service=\"$service\"}[5m]))",
"legendFormat": "Error Rate"
}
]
},
{
"id": 3,
"title": "Response Latency (p95)",
"type": "graph",
"gridPos": {"x": 0, "y": 8, "w": 12, "h": 8},
"targets": [
{
"expr": "histogram_quantile(0.95, rate(request_duration_seconds_bucket{service=\"$service\"}[5m]))",
"legendFormat": "p95"
}
]
},
{
"id": 4,
"title": "Active Connections",
"type": "stat",
"gridPos": {"x": 12, "y": 8, "w": 12, "h": 8},
"targets": [
{
"expr": "sum(active_connections{service=\"$service\"})"
}
]
}
]
}
}
2. Grafana Provisioning Configuration
# /etc/grafana/provisioning/dashboards/dashboards.yaml
apiVersion: 1
providers:
- name: 'Dashboards'
orgId: 1
folder: 'Production'
type: file
disableDeletion: false
updateIntervalSeconds: 10
options:
path: /var/lib/grafana/dashboards
# /etc/grafana/provisioning/datasources/prometheus.yaml
apiVersion: 1
datasources:
- name: Prometheus
type: prometheus
access: proxy
orgId: 1
url: http://prometheus:9090
isDefault: true
editable: true
jsonData:
timeInterval: '30s'
3. Grafana Alert Configuration
# /etc/grafana/provisioning/alerting/alerts.yaml
groups:
- name: application_alerts
interval: 1m
rules:
- uid: alert_high_error_rate
title: High Error Rate
condition: B
data:
- refId: A
model:
expr: 'sum(rate(requests_total{status_code=~"5.."}[5m]))'
- refId: B
conditions:
- evaluator:
params: [0.05]
type: gt
query:
params: [A, 5m, now]
for: 5m
annotations:
description: 'Error rate is {{ $values.A }}'
labels:
severity: critical
team: platform
4. Grafana API Client
// grafana-api-client.js
const axios = require('axios');
class GrafanaClient {
constructor(baseUrl, apiKey) {
this.baseUrl = baseUrl;
this.client = axios.create({
baseURL: baseUrl,
headers: {
'Authorization': `Bearer ${apiKey}`,
'Content-Type': 'application/json'
}
});
}
async createDashboard(dashboard) {
const response = await this.client.post('/api/dashboards/db', {
dashboard: dashboard,
overwrite: true
});
return response.data;
}
async getDashboard(uid) {
const response = await this.client.get(`/api/dashboards/uid/${uid}`);
return response.data;
}
async createAlert(alert) {
const response = await this.client.post('/api/alerts', alert);
return response.data;
}
async listDashboards() {
const response = await this.client.get('/api/search?query=');
return response.data;
}
}
module.exports = GrafanaClient;
5. Docker Compose Setup
version: '3.8'
services:
grafana:
image: grafana/grafana:latest
ports:
- "3000:3000"
environment:
GF_SECURITY_ADMIN_PASSWORD: ${GRAFANA_PASSWORD:-admin}
GF_USERS_ALLOW_SIGN_UP: 'false'
GF_SERVER_ROOT_URL: http://grafana.example.com
volumes:
- ./provisioning:/etc/grafana/provisioning
- grafana_storage:/var/lib/grafana
depends_on:
- prometheus
prometheus:
image: prom/prometheus:latest
ports:
- "9090:9090"
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
- prometheus_storage:/prometheus
volumes:
grafana_storage:
prometheus_storage:
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
Visualization Types
- Graph: Time-series trends
- Stat: Single value with thresholds
- Gauge: Percentage or usage
- Heatmap: Pattern detection
- Bar Chart: Category comparison
- Pie Chart: Composition
GitHub Repository
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