Grafana Dashboards

When to use this skill

Use Grafana Dashboards when an AI agent needs a reusable SKILL.md workflow for this job: 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.

When not to use it

Skip Grafana Dashboards when the task is outside the coding category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/grafana-dashboards/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/grafana-dashboards/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# 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

```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 `assets/api-dashboard.json`

## 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 `assets/infrastructure-dashboard.json`

### 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 `assets/database-dashboard.json`

### 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 ```

## Related Skills

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

Intended uses

• Visualize Prometheus metrics
• Create custom dashboards
• Implement SLO dashboards
• Monitor infrastructure
• Track business KPIs