Grafana Dashboards

by wshobson46891e7e60daNo licenseListed Oct 8, 2026Updated Oct 8, 2026

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.

Instructions onlyData & Analytics DevOps & Cloud

AI-generated overview

Creates and manages Grafana dashboards for visualizing system and application metrics.

What it does: This skill provides guidance and JSON examples for building production Grafana dashboards that visualize metrics such as request rate, error rate, latency, and resource usage. It covers panel types (stat, time series, table, heatmap), template variables, dashboard alerts, and provisioning via files, Terraform, or Ansible. It also outlines design principles like the RED and USE methods and common infrastructure, database, and application dashboard patterns.
When to use it: Use it when building monitoring dashboards, visualizing Prometheus metrics, implementing SLO dashboards, or creating operational observability interfaces. It suits teams that need consistent dashboard structure and provisioning practices.
Requirements: No scripts are included; it is instructions and JSON/YAML/HCL examples only. Producing live dashboards requires a Grafana instance and a metrics data source such as Prometheus, and provisioning examples assume tools like Terraform or Ansible.

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) │└─────────────────────────────────────┘

┌─────────────────────────────────────┐│  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

json

{  "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 }      }    ]  }}

{  "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)

json

{  "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" }        ]      }    }  }}

{  "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

json

{  "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" }  ]}

{  "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

json

{  "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"        }      }    }  ]}

{  "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

json

{  "type": "heatmap",  "title": "Latency Heatmap",  "targets": [    {      "expr": "sum(rate(http_request_duration_seconds_bucket[5m])) by (le)",      "format": "heatmap"    }  ],  "dataFormat": "tsbuckets",  "yAxis": {    "format": "s"  }}

{  "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

json

{  "templating": {    "list": [      {        "name": "namespace",        "type": "query",        "datasource": "Prometheus",        "query": "label_values(kube_pod_info, namespace)",        "refresh": 1,        "multi": false      },      {        "name": "service",        "type": "query",        "datasource": "Prometheus",        "query": "label_values(kube_service_info{namespace=\"$namespace\"}, service)",        "refresh": 1,        "multi": true      }    ]  }}

{  "templating": {    "list": [      {        "name": "namespace",        "type": "query",        "datasource": "Prometheus",        "query": "label_values(kube_pod_info, namespace)",        "refresh": 1,        "multi": false      },      {        "name": "service",        "type": "query",        "datasource": "Prometheus",        "query": "label_values(kube_service_info{namespace=\"$namespace\"}, service)",        "refresh": 1,        "multi": true      }    ]  }}

Use Variables in Queries

Alerts in Dashboards

json

{  "alert": {    "name": "High Error Rate",    "conditions": [      {        "evaluator": {          "params": [5],          "type": "gt"        },        "operator": { "type": "and" },        "query": {          "params": ["A", "5m", "now"]        },        "reducer": { "type": "avg" },        "type": "query"      }    ],    "executionErrorState": "alerting",    "for": "5m",    "frequency": "1m",    "message": "Error rate is above 5%",    "noDataState": "no_data",    "notifications": [{ "uid": "slack-channel" }]  }}

{  "alert": {    "name": "High Error Rate",    "conditions": [      {        "evaluator": {          "params": [5],          "type": "gt"        },        "operator": { "type": "and" },        "query": {          "params": ["A", "5m", "now"]        },        "reducer": { "type": "avg" },        "type": "query"      }    ],    "executionErrorState": "alerting",    "for": "5m",    "frequency": "1m",    "message": "Error rate is above 5%",    "noDataState": "no_data",    "notifications": [{ "uid": "slack-channel" }]  }}

Dashboard Provisioning

dashboards.yml:

yaml

apiVersion: 1
providers:  - name: "default"    orgId: 1    folder: "General"    type: file    disableDeletion: false    updateIntervalSeconds: 10    allowUiUpdates: true    options:      path: /etc/grafana/dashboards

apiVersion: 1
providers:  - name: "default"    orgId: 1    folder: "General"    type: file    disableDeletion: false    updateIntervalSeconds: 10    allowUiUpdates: true    options:      path: /etc/grafana/dashboards

Common Dashboard Patterns

Infrastructure Dashboard

Key Panels:

CPU utilization per node
Memory usage per node
Disk I/O
Network traffic
Pod count by namespace
Node status

Database Dashboard

Key Panels:

Queries per second
Connection pool usage
Query latency (P50, P95, P99)
Active connections
Database size
Replication lag
Slow queries

Application Dashboard

Key Panels:

Request rate
Error rate
Response time (percentiles)
Active users/sessions
Cache hit rate
Queue length

Best Practices

Start with templates (Grafana community dashboards)
Use consistent naming for panels and variables
Group related metrics in rows
Set appropriate time ranges (default: Last 6 hours)
Use variables for flexibility
Add panel descriptions for context
Configure units correctly
Set meaningful thresholds for colors
Use consistent colors across dashboards
Test with different time ranges

Dashboard as Code

Terraform Provisioning

hcl

resource "grafana_dashboard" "api_monitoring" {  config_json = file("${path.module}/dashboards/api-monitoring.json")  folder      = grafana_folder.monitoring.id}
resource "grafana_folder" "monitoring" {  title = "Production Monitoring"}

resource "grafana_dashboard" "api_monitoring" {  config_json = file("${path.module}/dashboards/api-monitoring.json")  folder      = grafana_folder.monitoring.id}
resource "grafana_folder" "monitoring" {  title = "Production Monitoring"}

Ansible Provisioning

yaml

- name: Deploy Grafana dashboards  copy:    src: "{{ item }}"    dest: /etc/grafana/dashboards/  with_fileglob:    - "dashboards/*.json"  notify: restart grafana

- name: Deploy Grafana dashboards  copy:    src: "{{ item }}"    dest: /etc/grafana/dashboards/  with_fileglob:    - "dashboards/*.json"  notify: restart grafana

Related Skills

prometheus-configuration - For metric collection
slo-implementation - For SLO dashboards

Source and attribution

Source:wshobson/agentsinplugins/observability-monitoring/skills/grafana-dashboardsat commit46891e7

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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Grafana Dashboards

Grafana Dashboards

Purpose

When to Use

Dashboard Design Principles

1. Hierarchy of Information

2. RED Method (Services)

3. USE Method (Resources)

Dashboard Structure

API Monitoring Dashboard

Panel Types

1. Stat Panel (Single Value)

2. Time Series Graph

3. Table Panel

4. Heatmap

Variables

Query Variables

Use Variables in Queries

Alerts in Dashboards

Dashboard Provisioning

Common Dashboard Patterns

Infrastructure Dashboard

Database Dashboard

Application Dashboard

Best Practices

Dashboard as Code

Terraform Provisioning

Ansible Provisioning

Related Skills

Source and attribution

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