Agent skill

grafana-dashboards

Build monitoring dashboards with Grafana. Covers panel types, queries, variables, alerting, provisioning, and data sources like Prometheus and InfluxDB. Use for infrastructure monitoring, observability, and metrics visualization.

View SKILL.md on GitHub Repository
Stars 163
Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/grafana-dashboards-fgarofalo56-suppercharge-microso

SKILL.md

Grafana Dashboards

Build powerful monitoring and observability dashboards.

Instructions

  1. Start with key metrics - CPU, memory, latency, error rates
  2. Use consistent time ranges - All panels should sync
  3. Add context with variables - Filter by environment, service, host
  4. Set up alerts - Proactive monitoring, not reactive
  5. Use templates - Consistent dashboard styling

Dashboard Structure

Dashboard JSON

json
{
  "dashboard": {
    "id": null,
    "uid": "my-dashboard",
    "title": "Service Overview",
    "tags": ["production", "service-name"],
    "timezone": "browser",
    "schemaVersion": 39,
    "version": 1,
    "refresh": "30s",
    "time": {
      "from": "now-1h",
      "to": "now"
    },
    "templating": {
      "list": []
    },
    "panels": [],
    "annotations": {
      "list": []
    }
  }
}

Panel Types

json
// Time series (line chart)
{
  "type": "timeseries",
  "title": "Request Rate",
  "gridPos": { "x": 0, "y": 0, "w": 12, "h": 8 },
  "fieldConfig": {
    "defaults": {
      "unit": "reqps",
      "custom": {
        "lineWidth": 2,
        "fillOpacity": 10,
        "gradientMode": "opacity"
      }
    }
  },
  "targets": [
    {
      "expr": "rate(http_requests_total{job=\"$job\"}[5m])",
      "legendFormat": "{{method}} {{status}}"
    }
  ]
}

// Stat panel (single value)
{
  "type": "stat",
  "title": "Total Requests",
  "gridPos": { "x": 0, "y": 0, "w": 4, "h": 4 },
  "options": {
    "colorMode": "value",
    "graphMode": "area",
    "justifyMode": "auto",
    "orientation": "auto",
    "reduceOptions": {
      "calcs": ["lastNotNull"],
      "fields": "",
      "values": false
    }
  },
  "targets": [
    {
      "expr": "sum(http_requests_total{job=\"$job\"})",
      "legendFormat": ""
    }
  ]
}

// Gauge
{
  "type": "gauge",
  "title": "CPU Usage",
  "fieldConfig": {
    "defaults": {
      "unit": "percent",
      "min": 0,
      "max": 100,
      "thresholds": {
        "mode": "absolute",
        "steps": [
          { "color": "green", "value": null },
          { "color": "yellow", "value": 70 },
          { "color": "red", "value": 90 }
        ]
      }
    }
  }
}

// Table
{
  "type": "table",
  "title": "Top Endpoints",
  "transformations": [
    {
      "id": "sortBy",
      "options": {
        "fields": {},
        "sort": [{ "field": "Value", "desc": true }]
      }
    }
  ]
}

Prometheus Queries (PromQL)

Basic Queries

promql
# Instant rate (requests per second)
rate(http_requests_total[5m])

# Sum by label
sum by (status_code) (rate(http_requests_total[5m]))

# Average latency
histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))

# Error rate percentage
sum(rate(http_requests_total{status=~"5.."}[5m])) /
sum(rate(http_requests_total[5m])) * 100

# CPU usage percentage
100 - (avg by(instance) (rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

# Memory usage
(node_memory_MemTotal_bytes - node_memory_MemAvailable_bytes) /
node_memory_MemTotal_bytes * 100

# Disk usage
(node_filesystem_size_bytes - node_filesystem_avail_bytes) /
node_filesystem_size_bytes * 100

Aggregation & Filtering

promql
# Filter by label
http_requests_total{job="api", environment="production"}

# Regex match
http_requests_total{path=~"/api/v[0-9]+/.*"}

# Not equal
http_requests_total{status!="200"}

# Rate over time window
rate(metric[5m])   # 5 minute rate
irate(metric[5m])  # Instant rate (last 2 points)

# Aggregations
sum(metric)              # Total
avg(metric)              # Average
max(metric)              # Maximum
min(metric)              # Minimum
count(metric)            # Count of series
topk(5, metric)          # Top 5 series
bottomk(5, metric)       # Bottom 5 series

# Group by label
sum by (instance) (metric)
avg without (instance) (metric)

Variables (Templating)

json
{
  "templating": {
    "list": [
      {
        "name": "datasource",
        "type": "datasource",
        "query": "prometheus",
        "current": {},
        "hide": 0
      },
      {
        "name": "environment",
        "type": "query",
        "datasource": "${datasource}",
        "query": "label_values(up, environment)",
        "refresh": 1,
        "multi": false,
        "includeAll": true,
        "allValue": ".*"
      },
      {
        "name": "instance",
        "type": "query",
        "datasource": "${datasource}",
        "query": "label_values(up{environment=\"$environment\"}, instance)",
        "refresh": 2,
        "multi": true,
        "includeAll": true
      },
      {
        "name": "interval",
        "type": "interval",
        "options": [
          { "selected": false, "text": "1m", "value": "1m" },
          { "selected": true, "text": "5m", "value": "5m" },
          { "selected": false, "text": "15m", "value": "15m" }
        ]
      }
    ]
  }
}

Usage in queries:

promql
rate(http_requests_total{environment=~"$environment", instance=~"$instance"}[$interval])

Alerting

Alert Rule

json
{
  "alert": "HighErrorRate",
  "expr": "sum(rate(http_requests_total{status=~\"5..\"}[5m])) / sum(rate(http_requests_total[5m])) > 0.05",
  "for": "5m",
  "labels": {
    "severity": "critical"
  },
  "annotations": {
    "summary": "High error rate detected",
    "description": "Error rate is {{ $value | humanizePercentage }} for the last 5 minutes"
  }
}

Grafana Alerting (v8+)

yaml
# provisioning/alerting/alerts.yaml
apigroups:
  - name: service-alerts
    folder: Alerts
    interval: 1m
    rules:
      - uid: high-error-rate
        title: High Error Rate
        condition: C
        data:
          - refId: A
            datasourceUid: prometheus
            model:
              expr: sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m])) * 100
          - refId: C
            datasourceUid: __expr__
            model:
              type: threshold
              conditions:
                - evaluator:
                    type: gt
                    params: [5]
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: Error rate above 5%

Dashboard Provisioning

File Structure

grafana/
├── provisioning/
│   ├── dashboards/
│   │   └── dashboards.yaml
│   ├── datasources/
│   │   └── datasources.yaml
│   └── alerting/
│       └── alerts.yaml
└── dashboards/
    ├── overview.json
    └── service-details.json

Datasources Config

yaml
# provisioning/datasources/datasources.yaml
apidatasources:
  - name: Prometheus
    type: prometheus
    access: proxy
    url: http://prometheus:9090
    isDefault: true
    editable: false

  - name: InfluxDB
    type: influxdb
    access: proxy
    url: http://influxdb:8086
    database: metrics
    user: admin
    secureJsonData:
      password: ${INFLUXDB_PASSWORD}

Dashboard Provider

yaml
# provisioning/dashboards/dashboards.yaml
apiproviders:
  - name: default
    orgId: 1
    folder: ''
    type: file
    disableDeletion: false
    editable: true
    options:
      path: /var/lib/grafana/dashboards

Common Dashboard Patterns

RED Method (Request, Error, Duration)

promql
# Request Rate
sum(rate(http_requests_total[5m]))

# Error Rate
sum(rate(http_requests_total{status=~"5.."}[5m])) /
sum(rate(http_requests_total[5m]))

# Duration (95th percentile)
histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))

USE Method (Utilization, Saturation, Errors)

promql
# CPU Utilization
100 - (avg(rate(node_cpu_seconds_total{mode="idle"}[5m])) * 100)

# Memory Saturation
node_memory_SwapCached_bytes / node_memory_SwapTotal_bytes

# Network Errors
rate(node_network_receive_errs_total[5m])

Best Practices

  1. Use consistent colors - Red for errors, green for success
  2. Add descriptions - Panel descriptions explain what's shown
  3. Set meaningful thresholds - Color changes at important values
  4. Link related dashboards - Drill-down from overview to details
  5. Version control dashboards - Store JSON in git
  6. Use dashboard folders - Organize by team or service

When to Use

  • Infrastructure monitoring
  • Application performance monitoring
  • Business metrics dashboards
  • Real-time operational dashboards
  • SLA/SLO tracking

Notes

  • Grafana Cloud offers managed hosting
  • Use Terraform provider for IaC
  • Consider Grafana Loki for logs
  • Grafana Tempo for distributed tracing

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-spec

Manage specification documents in .agent/specs/. Use when user provides requirements, acceptance criteria, or feature descriptions that need to be tracked and validated against implementation.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-testing

Test strategy, execution, and coverage analysis. Use when designing tests, running test suites, or analyzing test results beyond baseline checks.

163 31
Explore
majiayu000/claude-skill-registry

agent-ops-state

Maintain .agent state files. Use at session start, after meaningful steps, and before concluding: read/update constitution/memory/focus/issues/baseline consistently.

163 31
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results