Claude grafana skill: 10 dashboards you can ship as code

The prompt

Build a Grafana dashboard JSON for the `checkout` service. Metrics available: `http_requests_total`, `http_request_duration_seconds_bucket`, `http_requests_total{code=~"5.."}`. Order it RED-method: top row three stat panels (req/s, error %, p95 ms), second row two time-series (request rate by route, error rate). Datasource is a Prometheus datasource referenced by its UID, schemaVersion 39. Title 'Checkout — Service Health'. Save as `dashboards/checkout-health.json`.

What slides.md looks like

{
  "title": "Checkout — Service Health",
  "schemaVersion": 39,
  "panels": [
    {
      "type": "stat", "title": "Requests / sec",
      "gridPos": { "h": 4, "w": 6, "x": 0, "y": 0 },
      "datasource": { "type": "prometheus", "uid": "${DS_PROM}" },
      "targets": [
        { "expr": "sum(rate(http_requests_total{service=\"checkout\"}[5m]))" }
      ]
    },
    {
      "type": "timeseries", "title": "Request rate by route",
      "gridPos": { "h": 8, "w": 12, "x": 0, "y": 4 },
      "datasource": { "type": "prometheus", "uid": "${DS_PROM}" },
      "targets": [
        { "expr": "sum(rate(http_requests_total{service=\"checkout\"}[5m])) by (route)" }
      ]
    }
  ]
}

Pairs with

The prompt

Generate a Grafana time-series panel JSON that plots p95 request latency from `http_request_duration_seconds_bucket`, broken down by route. Use `histogram_quantile(0.95, ...)` summed by `le, route`. Set the field unit to seconds (`s`), add a red threshold step at 0.5, and title it 'p95 latency by route'. Prometheus datasource by UID.

What slides.md looks like

{
  "type": "timeseries",
  "title": "p95 latency by route",
  "datasource": { "type": "prometheus", "uid": "${DS_PROM}" },
  "fieldConfig": {
    "defaults": {
      "unit": "s",
      "thresholds": {
        "mode": "absolute",
        "steps": [
          { "value": null, "color": "green" },
          { "value": 0.5, "color": "red" }
        ]
      }
    }
  },
  "targets": [
    {
      "expr": "histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, route))",
      "legendFormat": "{{route}}"
    }
  ]
}

Pairs with

The prompt

Add templating to `dashboards/checkout-health.json`. Two query variables: `namespace` from `label_values(kube_pod_info, namespace)`, and `service` from `label_values(http_requests_total{namespace="$namespace"}, service)` with multi-value + 'All' enabled. Rewrite every panel target to filter `{namespace="$namespace", service=~"$service"}`. Keep the panel layout unchanged.

What slides.md looks like

"templating": {
  "list": [
    {
      "name": "namespace", "type": "query",
      "datasource": { "type": "prometheus", "uid": "${DS_PROM}" },
      "query": "label_values(kube_pod_info, namespace)",
      "refresh": 2, "sort": 1
    },
    {
      "name": "service", "type": "query",
      "datasource": { "type": "prometheus", "uid": "${DS_PROM}" },
      "query": "label_values(http_requests_total{namespace=\"$namespace\"}, service)",
      "includeAll": true, "multi": true, "refresh": 2
    }
  ]
}
// panel target becomes:
// sum(rate(http_requests_total{namespace="$namespace", service=~"$service"}[5m]))

Pairs with

The prompt

Write a Grafana alert rule (provisioning YAML, apiVersion 1) that fires when 5xx error rate on `checkout` exceeds 5% for 5 minutes. Use a reduce + threshold expression chain over `sum(rate(http_requests_total{service="checkout",code=~"5.."}[5m])) / sum(rate(http_requests_total{service="checkout"}[5m]))`. Label `severity: page`, `team: payments`. Then add a notification policy that routes `team=payments` to a `pagerduty-payments` receiver.

What slides.md looks like

apiVersion: 1
groups:
  - orgId: 1
    name: checkout-slo
    folder: Checkout
    interval: 1m
    rules:
      - title: Checkout 5xx error rate high
        condition: C
        data:
          - refId: A
            relativeTimeRange: { from: 600, to: 0 }
            datasourceUid: ${DS_PROM}
            model:
              expr: >
                sum(rate(http_requests_total{service="checkout",code=~"5.."}[5m]))
                / sum(rate(http_requests_total{service="checkout"}[5m]))
          - refId: C
            datasourceUid: __expr__
            model: { type: threshold, expression: A,
                     conditions: [ { evaluator: { type: gt, params: [0.05] } } ] }
        for: 5m
        labels: { severity: page, team: payments }

Pairs with

The prompt

Add a Loki logs panel to the bottom row of the checkout dashboard. Loki datasource by UID. LogQL: `{namespace="$namespace", app="checkout"} |= "error" | json | line_format "{{.level}} {{.msg}}"`. Full width, 10 rows tall, newest-first, wrap off. Title 'Checkout errors (Loki)'.

What slides.md looks like

{
  "type": "logs",
  "title": "Checkout errors (Loki)",
  "gridPos": { "h": 10, "w": 24, "x": 0, "y": 20 },
  "datasource": { "type": "loki", "uid": "${DS_LOKI}" },
  "options": { "showTime": true, "sortOrder": "Descending", "wrapLogMessage": false },
  "targets": [
    {
      "expr": "{namespace=\"$namespace\", app=\"checkout\"} |= \"error\" | json | line_format \"{{.level}} {{.msg}}\"",
      "queryType": "range"
    }
  ]
}

Pairs with

The prompt

Take `dashboards/checkout-health.json` and make it provision-ready. Write `provisioning/dashboards/dashboards.yml` (apiVersion 1) pointing a file provider at `/var/lib/grafana/dashboards`, `updateIntervalSeconds: 10`, `disableDeletion: false`, `allowUiUpdates: false` so UI edits can't silently fork the file. Strip the dashboard `id` to null and pin `uid: checkout-health` so the provisioned dashboard is stable across reloads.

What slides.md looks like

# provisioning/dashboards/dashboards.yml
apiVersion: 1
providers:
  - name: 'service-dashboards'
    orgId: 1
    folder: 'Services'
    type: file
    disableDeletion: false
    updateIntervalSeconds: 10
    allowUiUpdates: false
    options:
      path: /var/lib/grafana/dashboards
      foldersFromFilesStructure: true

# dashboards/checkout-health.json (head)
# { "id": null, "uid": "checkout-health", "title": "Checkout — Service Health", ... }

Pairs with

The prompt

Add an `instance` query variable (`label_values(node_cpu_seconds_total, instance)`, multi, includeAll) to a node dashboard. Create a row titled '$instance' with `repeat: instance` and `repeatDirection: h`, containing one time-series panel that graphs CPU busy %: `100 - (avg by (instance)(rate(node_cpu_seconds_total{mode="idle", instance="$instance"}[5m])) * 100)`. Grafana renders one row per selected instance.

What slides.md looks like

{
  "type": "row",
  "title": "$instance",
  "repeat": "instance",
  "repeatDirection": "h",
  "panels": [
    {
      "type": "timeseries", "title": "CPU busy %",
      "fieldConfig": { "defaults": { "unit": "percent", "max": 100 } },
      "datasource": { "type": "prometheus", "uid": "${DS_PROM}" },
      "targets": [
        { "expr": "100 - (avg by (instance)(rate(node_cpu_seconds_total{mode=\"idle\", instance=\"$instance\"}[5m])) * 100)" }
      ]
    }
  ]
}

Pairs with

The prompt

Build an SLO burn-rate dashboard for a 99.9% availability target on `checkout`. Define good = non-5xx requests. Panels: remaining error budget (stat, %), 1h burn rate, 6h burn rate, and a time-series of both burn rates with a threshold line at 1. Burn rate = (1 - (sum(rate(good[w])) / sum(rate(total[w])))) / (1 - 0.999). Annotate that >14.4 over 1h is a fast-burn page.

What slides.md looks like

// 1h burn rate target
(
  1 - (
    sum(rate(http_requests_total{service="checkout",code!~"5.."}[1h]))
    /
    sum(rate(http_requests_total{service="checkout"}[1h]))
  )
) / (1 - 0.999)

// remaining 30d error budget (stat panel, unit "percentunit")
1 - (
  (1 - (sum(increase(http_requests_total{service="checkout",code!~"5.."}[30d]))
        / sum(increase(http_requests_total{service="checkout"}[30d]))))
  / (1 - 0.999)
)

Pairs with

The prompt

I imported Grafana.com dashboard 1860 (Node Exporter Full). Every panel shows 'datasource not found' because it references a datasource named 'Prometheus' and mine is a UID. Rewrite the JSON so every `datasource` field uses `{ "type": "prometheus", "uid": "$DS_PROM" }`, add a dashboard-level `__inputs` entry for `DS_PROM`, and convert any bytes panels to use the `bytes` unit. Leave the queries untouched.

What slides.md looks like

"__inputs": [
  {
    "name": "DS_PROM",
    "label": "Prometheus",
    "type": "datasource",
    "pluginId": "prometheus"
  }
],
// every panel + target datasource rewritten from:
//   "datasource": "Prometheus"
// to:
//   "datasource": { "type": "prometheus", "uid": "${DS_PROM}" }

Pairs with

The prompt

Add a built-in annotation to the checkout dashboard that shows deploy markers tagged `deploy` and `checkout` as a vertical line. Then write the `curl` for our GitHub Actions deploy job to POST to Grafana's `/api/annotations` with the current epoch-ms `time`, tags `["deploy","checkout"]`, and text `Deployed ${GITHUB_SHA}`. Use a service-account token from `$GRAFANA_TOKEN`.

What slides.md looks like

// dashboard annotations block
"annotations": { "list": [
  {
    "name": "Deploys", "enable": true, "iconColor": "purple",
    "datasource": { "type": "grafana", "uid": "-- Grafana --" },
    "target": { "type": "tags", "tags": ["deploy", "checkout"], "limit": 100 }
  }
]}

# CI step
curl -s -X POST "$GRAFANA_URL/api/annotations" \
  -H "Authorization: Bearer $GRAFANA_TOKEN" \
  -H "Content-Type: application/json" \
  -d "{\"time\": $(date +%s%3N),
       \"tags\": [\"deploy\",\"checkout\"],
       \"text\": \"Deployed $GITHUB_SHA\"}"

Pairs with