sickn33/antigravity-awesome-skills/grafana-dashboards

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Create and manage production-ready Grafana dashboards for comprehensive system observability.

Install

npx vskill@latest install sickn33/antigravity-awesome-skills/grafana-dashboards

SKILL.md

Grafana Dashboards

Create and manage production-ready Grafana dashboards for comprehensive system observability.

Do not use this skill when

  • The task is unrelated to grafana dashboards
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Purpose

Design effective Grafana dashboards for monitoring applications, infrastructure, and business metrics.

Use this skill when

  • 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

The following JSON uses legacy graph panels and embedded alert fields. It illustrates query intent, not a portable current provisioning artifact. Export supported panel JSON from the target Grafana version; configure alert rules separately through its supported alerting interface. Test missing/zero traffic and verify datasource UIDs before deployment.

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}
      }
    ]
  }
}

Reference: See inline example

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"}
        ]
      }
    }
  }
}
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"}
  ]
}
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"
        }
      }
    }
  ]
}
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"
  }
}

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=\"$namespace\"}, service)",
        "refresh": 1,
        "multi": true
      }
    ]
  }
}
Use Variables in Queries
sum(rate(http_requests_total{namespace="$namespace", service=~"$service"}[5m]))

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"}
    ]
  }
}

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

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

Reference: See inline example

Database Dashboard

Key Panels:

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

Reference: See inline example

Application Dashboard

Key Panels:

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

Best Practices

  1. Start with templates (Grafana community dashboards)
  2. Use consistent naming for panels and variables
  3. Group related metrics in rows
  4. Set appropriate time ranges (default: Last 6 hours)
  5. Use variables for flexibility
  6. Add panel descriptions for context
  7. Configure units correctly
  8. Set meaningful thresholds for colors
  9. Use consistent colors across dashboards
  10. 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"
}
Ansible Provisioning
yaml
- name: Deploy Grafana dashboards
  copy:
    src: "{{ item }}"
    dest: /etc/grafana/dashboards/
  with_fileglob:
    - "dashboards/*.json"
  notify: restart grafana

Reference Files

Related Skills

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

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

See the full security report

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

See evaluation results

Source

Popularity

Repository stars
27,697
Repository forks
4,655
Repository npm downloads, 7 days
9,936
7-day trend score
73

Stars and forks are for the whole GitHub repository, not this skill alone. Downloads are for the repository's npm package. Refreshed 25 Mar 2026, 7 months ago.

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