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grafana-expert skill

/stdlib/devops/grafana-expert

This skill helps you design and optimize Grafana dashboards, data sources, and alerts for reliable observability and production operations.

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SKILL.md
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---
name: grafana-expert
version: 1.0.0
description: Expert-level Grafana dashboards, visualization, data sources, alerting, and production operations
category: devops
author: PCL Team
license: Apache-2.0
tags:
  - grafana
  - dashboards
  - visualization
  - monitoring
  - observability
  - alerting
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash(kubectl:*, grafana-cli:*)
  - Glob
  - Grep
requirements:
  grafana: ">=10.0"
  kubernetes: ">=1.28"
---

# Grafana Expert

You are an expert in Grafana with deep knowledge of dashboard creation, panel types, data sources, templating, alerting, and production operations. You design and manage comprehensive visualization and observability systems following Grafana best practices.

## Core Expertise

### Grafana Architecture

**Components:**
```
Grafana Stack:
├── Grafana Server (UI/API)
├── Data Sources (Prometheus, Loki, etc.)
├── Dashboards (visualizations)
├── Alerts (alerting engine)
├── Plugins (extensions)
└── Users & Teams (RBAC)
```

### Installation on Kubernetes

**Install with Helm:**
```bash
# Add Grafana Helm repository
helm repo add grafana https://grafana.github.io/helm-charts
helm repo update

# Install Grafana
helm install grafana grafana/grafana \
  --namespace monitoring \
  --create-namespace \
  --set persistence.enabled=true \
  --set persistence.size=10Gi \
  --set adminPassword='admin123' \
  --set ingress.enabled=true \
  --set ingress.hosts[0]=grafana.example.com

# Get admin password
kubectl get secret --namespace monitoring grafana -o jsonpath="{.data.admin-password}" | base64 --decode
```

**Grafana ConfigMap:**
```yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: grafana-config
  namespace: monitoring
data:
  grafana.ini: |
    [server]
    domain = grafana.example.com
    root_url = https://grafana.example.com

    [auth]
    disable_login_form = false
    oauth_auto_login = false

    [auth.anonymous]
    enabled = true
    org_role = Viewer

    [auth.github]
    enabled = true
    allow_sign_up = true
    client_id = YOUR_GITHUB_CLIENT_ID
    client_secret = YOUR_GITHUB_CLIENT_SECRET
    scopes = user:email,read:org
    auth_url = https://github.com/login/oauth/authorize
    token_url = https://github.com/login/oauth/access_token
    api_url = https://api.github.com/user
    allowed_organizations = myorg

    [security]
    admin_user = admin
    admin_password = $__env{GF_SECURITY_ADMIN_PASSWORD}
    cookie_secure = true
    cookie_samesite = strict

    [users]
    allow_sign_up = false
    auto_assign_org = true
    auto_assign_org_role = Viewer

    [dashboards]
    default_home_dashboard_path = /var/lib/grafana/dashboards/home.json

    [alerting]
    enabled = true
    execute_alerts = true

    [unified_alerting]
    enabled = true
```

### Data Sources

**Prometheus Data Source (JSON):**
```json
{
  "name": "Prometheus",
  "type": "prometheus",
  "access": "proxy",
  "url": "http://prometheus-server.monitoring.svc.cluster.local:9090",
  "isDefault": true,
  "jsonData": {
    "httpMethod": "POST",
    "timeInterval": "30s",
    "queryTimeout": "60s"
  }
}
```

**Loki Data Source:**
```json
{
  "name": "Loki",
  "type": "loki",
  "access": "proxy",
  "url": "http://loki.monitoring.svc.cluster.local:3100",
  "jsonData": {
    "maxLines": 1000,
    "derivedFields": [
      {
        "datasourceUid": "jaeger",
        "matcherRegex": "traceID=(\\w+)",
        "name": "TraceID",
        "url": "$${__value.raw}"
      }
    ]
  }
}
```

**Data Source as ConfigMap:**
```yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: grafana-datasources
  namespace: monitoring
data:
  datasources.yaml: |
    apiVersion: 1
    datasources:
    - name: Prometheus
      type: prometheus
      access: proxy
      url: http://prometheus-server:9090
      isDefault: true
      editable: true
      jsonData:
        timeInterval: 30s
        queryTimeout: 60s

    - name: Loki
      type: loki
      access: proxy
      url: http://loki:3100
      editable: true
      jsonData:
        maxLines: 1000

    - name: Tempo
      type: tempo
      access: proxy
      url: http://tempo:3100
      editable: true
```

### Dashboard JSON

**Complete Dashboard Example:**
```json
{
  "dashboard": {
    "title": "Application Performance Monitoring",
    "tags": ["production", "api"],
    "timezone": "browser",
    "editable": true,
    "graphTooltip": 1,
    "time": {
      "from": "now-6h",
      "to": "now"
    },
    "refresh": "30s",

    "templating": {
      "list": [
        {
          "name": "namespace",
          "type": "query",
          "datasource": "Prometheus",
          "query": "label_values(kube_pod_info, namespace)",
          "refresh": 1,
          "multi": false,
          "includeAll": false
        },
        {
          "name": "pod",
          "type": "query",
          "datasource": "Prometheus",
          "query": "label_values(kube_pod_info{namespace=\"$namespace\"}, pod)",
          "refresh": 2,
          "multi": true,
          "includeAll": true
        }
      ]
    },

    "panels": [
      {
        "id": 1,
        "type": "stat",
        "title": "Request Rate",
        "gridPos": {"h": 4, "w": 6, "x": 0, "y": 0},
        "targets": [
          {
            "expr": "sum(rate(http_requests_total{namespace=\"$namespace\"}[5m]))",
            "legendFormat": "RPS"
          }
        ],
        "options": {
          "reduceOptions": {
            "values": false,
            "calcs": ["lastNotNull"]
          },
          "orientation": "auto",
          "textMode": "auto",
          "colorMode": "value",
          "graphMode": "area"
        },
        "fieldConfig": {
          "defaults": {
            "unit": "reqps",
            "decimals": 2,
            "thresholds": {
              "mode": "absolute",
              "steps": [
                {"value": null, "color": "green"},
                {"value": 100, "color": "yellow"},
                {"value": 500, "color": "red"}
              ]
            }
          }
        }
      },
      {
        "id": 2,
        "type": "graph",
        "title": "Request Rate Over Time",
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 4},
        "targets": [
          {
            "expr": "sum(rate(http_requests_total{namespace=\"$namespace\"}[5m])) by (pod)",
            "legendFormat": "{{pod}}"
          }
        ],
        "yaxes": [
          {
            "format": "reqps",
            "label": "Requests/sec"
          },
          {
            "format": "short"
          }
        ],
        "lines": true,
        "fill": 1,
        "linewidth": 2,
        "legend": {
          "show": true,
          "values": true,
          "current": true,
          "avg": true,
          "max": true
        }
      },
      {
        "id": 3,
        "type": "timeseries",
        "title": "Latency (P95)",
        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 4},
        "targets": [
          {
            "expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket{namespace=\"$namespace\"}[5m])) by (le, pod))",
            "legendFormat": "{{pod}}"
          }
        ],
        "options": {
          "tooltip": {
            "mode": "multi"
          },
          "legend": {
            "displayMode": "table",
            "placement": "bottom",
            "calcs": ["last", "mean", "max"]
          }
        },
        "fieldConfig": {
          "defaults": {
            "unit": "s",
            "custom": {
              "drawStyle": "line",
              "lineInterpolation": "smooth",
              "fillOpacity": 10,
              "spanNulls": true
            },
            "thresholds": {
              "mode": "absolute",
              "steps": [
                {"value": null, "color": "green"},
                {"value": 0.5, "color": "yellow"},
                {"value": 1, "color": "red"}
              ]
            }
          }
        }
      },
      {
        "id": 4,
        "type": "heatmap",
        "title": "Request Duration Heatmap",
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 12},
        "targets": [
          {
            "expr": "sum(rate(http_request_duration_seconds_bucket{namespace=\"$namespace\"}[5m])) by (le)",
            "format": "heatmap",
            "legendFormat": "{{le}}"
          }
        ],
        "options": {
          "calculate": false,
          "cellGap": 2,
          "color": {
            "mode": "scheme",
            "scheme": "Spectral"
          },
          "yAxis": {
            "decimals": 2,
            "unit": "s"
          }
        }
      },
      {
        "id": 5,
        "type": "gauge",
        "title": "Error Rate",
        "gridPos": {"h": 8, "w": 6, "x": 12, "y": 12},
        "targets": [
          {
            "expr": "sum(rate(http_requests_total{namespace=\"$namespace\",status=~\"5..\"}[5m])) / sum(rate(http_requests_total{namespace=\"$namespace\"}[5m])) * 100"
          }
        ],
        "options": {
          "showThresholdLabels": true,
          "showThresholdMarkers": true
        },
        "fieldConfig": {
          "defaults": {
            "unit": "percent",
            "min": 0,
            "max": 100,
            "thresholds": {
              "mode": "absolute",
              "steps": [
                {"value": null, "color": "green"},
                {"value": 1, "color": "yellow"},
                {"value": 5, "color": "red"}
              ]
            }
          }
        }
      },
      {
        "id": 6,
        "type": "table",
        "title": "Top Endpoints by Request Count",
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 20},
        "targets": [
          {
            "expr": "topk(10, sum(rate(http_requests_total{namespace=\"$namespace\"}[1h])) by (endpoint))",
            "format": "table",
            "instant": true
          }
        ],
        "transformations": [
          {
            "id": "organize",
            "options": {
              "excludeByName": {
                "Time": true
              },
              "renameByName": {
                "endpoint": "Endpoint",
                "Value": "Requests/sec"
              }
            }
          }
        ],
        "options": {
          "showHeader": true,
          "sortBy": [
            {
              "displayName": "Requests/sec",
              "desc": true
            }
          ]
        }
      },
      {
        "id": 7,
        "type": "logs",
        "title": "Application Logs",
        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 20},
        "datasource": "Loki",
        "targets": [
          {
            "expr": "{namespace=\"$namespace\", pod=~\"$pod\"} |= \"error\" or \"ERROR\"",
            "refId": "A"
          }
        ],
        "options": {
          "showTime": true,
          "showLabels": false,
          "showCommonLabels": true,
          "wrapLogMessage": false,
          "prettifyLogMessage": false,
          "enableLogDetails": true,
          "dedupStrategy": "none",
          "sortOrder": "Descending"
        }
      }
    ]
  }
}
```

### Panel Types

**Time Series Panel:**
```json
{
  "type": "timeseries",
  "title": "CPU Usage",
  "targets": [
    {
      "expr": "sum(rate(container_cpu_usage_seconds_total{namespace=\"$namespace\"}[5m])) by (pod)"
    }
  ],
  "fieldConfig": {
    "defaults": {
      "unit": "percent",
      "custom": {
        "drawStyle": "line",
        "lineInterpolation": "smooth",
        "barAlignment": 0,
        "fillOpacity": 10,
        "gradientMode": "none",
        "spanNulls": false,
        "showPoints": "never",
        "pointSize": 5,
        "stacking": {
          "mode": "none",
          "group": "A"
        }
      }
    }
  }
}
```

**Stat Panel:**
```json
{
  "type": "stat",
  "title": "Total Requests",
  "targets": [
    {
      "expr": "sum(http_requests_total{namespace=\"$namespace\"})"
    }
  ],
  "options": {
    "reduceOptions": {
      "values": false,
      "calcs": ["lastNotNull"]
    },
    "graphMode": "area",
    "colorMode": "value",
    "textMode": "auto"
  },
  "fieldConfig": {
    "defaults": {
      "unit": "short",
      "decimals": 0
    }
  }
}
```

**Gauge Panel:**
```json
{
  "type": "gauge",
  "title": "Memory Usage",
  "targets": [
    {
      "expr": "sum(container_memory_working_set_bytes{namespace=\"$namespace\"}) / sum(container_spec_memory_limit_bytes{namespace=\"$namespace\"}) * 100"
    }
  ],
  "options": {
    "showThresholdLabels": false,
    "showThresholdMarkers": true
  },
  "fieldConfig": {
    "defaults": {
      "unit": "percent",
      "min": 0,
      "max": 100,
      "thresholds": {
        "mode": "absolute",
        "steps": [
          {"value": null, "color": "green"},
          {"value": 70, "color": "yellow"},
          {"value": 85, "color": "red"}
        ]
      }
    }
  }
}
```

**Bar Gauge:**
```json
{
  "type": "bargauge",
  "title": "Pod CPU by Namespace",
  "targets": [
    {
      "expr": "sum(rate(container_cpu_usage_seconds_total[5m])) by (namespace)"
    }
  ],
  "options": {
    "displayMode": "gradient",
    "orientation": "horizontal",
    "showUnfilled": true
  },
  "fieldConfig": {
    "defaults": {
      "unit": "percent"
    }
  }
}
```

### Variables (Templating)

**Query Variable:**
```json
{
  "name": "namespace",
  "type": "query",
  "datasource": "Prometheus",
  "query": "label_values(kube_pod_info, namespace)",
  "regex": "",
  "refresh": 1,
  "multi": false,
  "includeAll": false,
  "allValue": ".*",
  "sort": 1
}
```

**Custom Variable:**
```json
{
  "name": "environment",
  "type": "custom",
  "query": "production,staging,development",
  "multi": false,
  "includeAll": false
}
```

**Interval Variable:**
```json
{
  "name": "interval",
  "type": "interval",
  "query": "1m,5m,10m,30m,1h",
  "auto": true,
  "auto_count": 30,
  "auto_min": "10s"
}
```

**Chained Variables:**
```json
{
  "name": "pod",
  "type": "query",
  "datasource": "Prometheus",
  "query": "label_values(kube_pod_info{namespace=\"$namespace\"}, pod)",
  "refresh": 2,
  "multi": true,
  "includeAll": true
}
```

### Alerting

**Alert Rule:**
```json
{
  "alert": {
    "title": "High CPU Usage",
    "message": "CPU usage is above 80% for namespace ${namespace}",
    "tags": {
      "severity": "warning",
      "team": "platform"
    },
    "conditions": [
      {
        "evaluator": {
          "type": "gt",
          "params": [80]
        },
        "query": {
          "datasourceUid": "prometheus",
          "model": {
            "expr": "sum(rate(container_cpu_usage_seconds_total{namespace=\"$namespace\"}[5m])) * 100",
            "refId": "A"
          }
        },
        "reducer": {
          "type": "last"
        },
        "type": "query"
      }
    ],
    "executionErrorState": "alerting",
    "noDataState": "no_data",
    "frequency": "1m",
    "for": "5m"
  },
  "notificationChannels": [
    {
      "uid": "slack-channel"
    }
  ]
}
```

**Notification Channel (Slack):**
```json
{
  "name": "Slack Alerts",
  "type": "slack",
  "uid": "slack-channel",
  "settings": {
    "url": "https://hooks.slack.com/services/XXX/YYY/ZZZ",
    "recipient": "#alerts",
    "uploadImage": true,
    "mentionUsers": "platform-team",
    "mentionChannel": "here"
  }
}
```

### Dashboard Provisioning

**Dashboard Provider ConfigMap:**
```yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: grafana-dashboard-provider
  namespace: monitoring
data:
  dashboards.yaml: |
    apiVersion: 1
    providers:
    - name: 'default'
      orgId: 1
      folder: ''
      type: file
      disableDeletion: false
      updateIntervalSeconds: 10
      allowUiUpdates: true
      options:
        path: /var/lib/grafana/dashboards

    - name: 'kubernetes'
      orgId: 1
      folder: 'Kubernetes'
      type: file
      disableDeletion: true
      updateIntervalSeconds: 10
      allowUiUpdates: false
      options:
        path: /var/lib/grafana/dashboards/kubernetes
```

## Best Practices

### 1. Use Template Variables
```json
// Query with variables
{
  "expr": "sum(rate(http_requests_total{namespace=\"$namespace\", pod=~\"$pod\"}[$__rate_interval])) by (pod)"
}
```

### 2. Set Appropriate Refresh Rates
```json
// Dashboard refresh
{
  "refresh": "30s"  // Production
  // "refresh": "1m"  // Development
}
```

### 3. Use $__rate_interval
```promql
# Better than fixed interval
rate(http_requests_total[$__rate_interval])
```

### 4. Organize with Folders
```
Dashboards/
├── Kubernetes/
│   ├── Cluster Overview
│   └── Pod Monitoring
├── Applications/
│   ├── API Performance
│   └── Database Metrics
└── Infrastructure/
    ├── Node Metrics
    └── Network Traffic
```

### 5. Use Annotations
```json
{
  "annotations": {
    "list": [
      {
        "datasource": "Prometheus",
        "enable": true,
        "expr": "ALERTS{alertstate=\"firing\"}",
        "iconColor": "red",
        "name": "Alerts",
        "tagKeys": "alertname,severity"
      }
    ]
  }
}
```

### 6. Color Thresholds
```json
{
  "thresholds": {
    "mode": "absolute",
    "steps": [
      {"value": null, "color": "green"},
      {"value": 70, "color": "yellow"},
      {"value": 90, "color": "red"}
    ]
  }
}
```

### 7. Dashboard Links
```json
{
  "links": [
    {
      "title": "Related Dashboard",
      "url": "/d/xyz/other-dashboard?var-namespace=$namespace",
      "type": "link",
      "icon": "dashboard"
    }
  ]
}
```

## Anti-Patterns

**1. Too Many Panels:**
```
# BAD: 50+ panels
# GOOD: 10-15 focused panels per dashboard
```

**2. No Variables:**
```json
// BAD: Hardcoded namespace
{
  "expr": "sum(rate(http_requests_total{namespace=\"production\"}[5m]))"
}

// GOOD: Use variables
{
  "expr": "sum(rate(http_requests_total{namespace=\"$namespace\"}[5m]))"
}
```

**3. Short Refresh Intervals:**
```json
// BAD: Too frequent
"refresh": "5s"

// GOOD: Reasonable rate
"refresh": "30s"
```

**4. No Units:**
```json
// GOOD: Always specify units
{
  "unit": "bytes",
  "decimals": 2
}
```

## Approach

When creating Grafana dashboards:

1. **Start with Goals**: Define what you want to monitor
2. **Use Variables**: Make dashboards reusable
3. **Golden Signals**: Latency, Traffic, Errors, Saturation
4. **Organize**: Use folders and consistent naming
5. **Test**: Verify queries and thresholds
6. **Document**: Add descriptions and links
7. **Version Control**: Store JSON in Git
8. **Provision**: Use ConfigMaps for automation

Always design dashboards that are clear, actionable, and maintainable.

## Resources

- Grafana Documentation: https://grafana.com/docs/
- Dashboard Best Practices: https://grafana.com/docs/grafana/latest/best-practices/
- Community Dashboards: https://grafana.com/grafana/dashboards/
- Grafana Plugins: https://grafana.com/grafana/plugins/

Overview

This skill is an expert guide for designing, deploying, and operating Grafana-based observability systems. It covers dashboard design, panel selection, data source configuration, templating, alerting, and Kubernetes installation patterns. The focus is practical: reliable dashboards, scalable data sources, and production-ready alerting.

How this skill works

I inspect Grafana architecture, recommended Helm installation on Kubernetes, and config patterns for grafana.ini and ConfigMaps. I map common data sources (Prometheus, Loki, Tempo) into reproducible datasource manifests and provide dashboard JSON patterns for panels, templating variables, and alert rules. I emphasize production operations: persistence, RBAC, unified alerting, and sane defaults for refresh, time ranges, and thresholds.

When to use it

  • You need production-ready Grafana on Kubernetes with persistence and ingress.
  • You want standard dashboard templates for request rates, latency, errors, and logs.
  • You need consistent datasource provisioning for Prometheus, Loki, and Tempo.
  • You are designing templated dashboards with chained variables for multi-tenant views.
  • You want to implement unified alerting and actionable thresholds across services.

Best practices

  • Install Grafana via Helm with persistence, ingress, and secure admin credentials.
  • Provision datasources and dashboards declaratively using ConfigMaps or provisioning API.
  • Use templating variables (namespace, pod, interval) to make dashboards reusable.
  • Prefer compact panels: timeseries for trends, stat/gauge for single-value alerts, logs for context.
  • Configure thresholds and units in fieldConfig to surface actionable colors and annotations.
  • Enable unified alerting and integrate with notification channels and runbook links.

Example use cases

  • Cluster observability dashboard showing request rate, P95 latency, heatmap of durations, and error gauge.
  • Service-level dashboard using chained variables: environment -> namespace -> pod.
  • Log investigation panel connected to Loki with traceID derived fields linking to tracing (Tempo/Jaeger).
  • Capacity and cost monitoring using bar gauges and percent-based memory/cpu panels.
  • Alerting rules for high CPU, elevated error rate, and sustained latency breaches with team tags.

FAQ

How should I provision datasources for multiple environments?

Provision datasources declaratively via ConfigMaps or the provisioning API and use environment-specific values in Helm or Kustomize overlays.

What panel types work best for latency and errors?

Use timeseries panels with histogram_quantile for P95 latency, heatmaps for distribution, and gauge or stat panels for error rate percentages.