Observability Monitoring

Skill 115 of 183

Grafana Dashboards

Create and manage production Grafana dashboards for real-time visualization of system and application metrics.

1 minute · 270 words · 27 sections

Install

$ npx skills add wshobson/agents --skill grafana-dashboards

$ npx skills add wshobson/agents

» /plugin marketplace add wshobson/agents

The first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.

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

{
  "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)

{
  "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

{
  "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

{
  "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

{
  "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

{
  "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

{
  "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:

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

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

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

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

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

About this skill

Trigger

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.

The verbatim description from this skill’s front matter — the string an agent matches on to decide whether to load it.

Colophon for this issue

Colophon

How this issue was made

Source

github.com/wshobson/agents, branch main, last pushed 21 September 2026.

Licence

MIT — the text of every skill is reproduced unmodified, frontmatter included, under the upstream licence.

Discovery

183 skills found by walking the repository tree for SKILL.md, not by matching a directory convention. 51 distinct layouts observed: plugins/accessibility-compliance/skills/*/SKILL.md, plugins/agent-teams/skills/*/SKILL.md, plugins/api-scaffolding/skills/*/SKILL.md, plugins/avoid-ai-writing/skills/*/SKILL.md, plugins/backend-development/skills/*/SKILL.md, plugins/before-you-build/skills/*/SKILL.md, plugins/block-no-verify/skills/*/SKILL.md, plugins/blockchain-web3/skills/*/SKILL.md, plugins/brand-landingpage/skills/*/SKILL.md, plugins/business-analytics/skills/*/SKILL.md, plugins/cicd-automation/skills/*/SKILL.md, plugins/cloud-infrastructure/skills/*/SKILL.md, plugins/conductor/skills/*/SKILL.md, plugins/data-engineering/skills/*/SKILL.md, plugins/database-design/skills/*/SKILL.md, plugins/developer-essentials/skills/*/SKILL.md, plugins/dgx-spark-ops/skills/*/SKILL.md, plugins/documentation-generation/skills/*/SKILL.md, plugins/documentation-standards/skills/*/SKILL.md, plugins/dotnet-contribution/skills/*/SKILL.md, plugins/file-conversion/skills/*/SKILL.md, plugins/framework-migration/skills/*/SKILL.md, plugins/frontend-mobile-development/skills/*/SKILL.md, plugins/game-development/skills/*/SKILL.md, plugins/hermes-tweet/skills/*/SKILL.md, plugins/hr-legal-compliance/skills/*/SKILL.md, plugins/incident-response/skills/*/SKILL.md, plugins/javascript-typescript/skills/*/SKILL.md, plugins/kubernetes-operations/skills/*/SKILL.md, plugins/llm-application-dev/skills/*/SKILL.md, plugins/llm-finetuning/skills/*/SKILL.md, plugins/machine-learning-ops/skills/*/SKILL.md, plugins/observability-monitoring/skills/*/SKILL.md, plugins/payment-processing/skills/*/SKILL.md, plugins/plugin-eval/skills/*/SKILL.md, plugins/pptx-deck-creation/skills/*/SKILL.md, plugins/protect-mcp/skills/*/SKILL.md, plugins/python-development/skills/*/SKILL.md, plugins/quantitative-trading/skills/*/SKILL.md, plugins/reverse-engineering/skills/*/SKILL.md, plugins/review-agent-governance/skills/*/SKILL.md, plugins/security-scanning/skills/*/SKILL.md, plugins/shell-scripting/skills/*/SKILL.md, plugins/ship-mate/skills/*/SKILL.md, plugins/signed-audit-trails/skills/*/SKILL.md, plugins/skill-forge-essentials/skills/*/SKILL.md, plugins/social-publishing/skills/*/SKILL.md, plugins/startup-business-analyst/skills/*/SKILL.md, plugins/superself/skills/*/SKILL.md, plugins/systems-programming/skills/*/SKILL.md, plugins/ui-design/skills/*/SKILL.md.

Issue colours

Resolved from a deterministic hash of the owner name. Two accent tones are generated per issue and each is proven against its own ground before it ships: a single accent that passes AA on both light and dark paper is arithmetically impossible.

Heading repairs

1 repair applied to this skill so the document has one h1 and no skipped levels:

Removed “Grafana Dashboards”, a leading h1 that duplicated the skill title.

Spec compliance

Every skill satisfies the Agent Skills specification’s frontmatter rules.

Images

Images inside a skill come from the upstream repository. Where the author gave no alternative text we mark the image decorative rather than inventing a description — a plausible caption we made up is worse than none for the reader who depends on it.

Marketplace

A plugin manifest is published at .claude-plugin/marketplace.json by Seth Hobson, declaring 94 plugins. It is read for editorial metadata only — never as the skill index, which is always the repository tree.

Signal

Install counts come from skills.sh. They measure downloads, not quality, and an unranked repository is not an unread one.

Agent surfaces

The whole issue is available as one markdown document at /wshobson/agents.md, and each skill at its own .md URL.

Publication

Set by Skills Docs from the source repository. Body text is Literata at the reader’s chosen size and measure; code is Geist Mono. Nothing on this page was written by us except this paragraph.

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

{
  "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)

{
  "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

{
  "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

{
  "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

{
  "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

{
  "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

{
  "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:

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

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

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

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

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