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Expert skill for Prometheus metrics and Grafana dashboards. Write and validate PromQL queries, generate Grafana dashboard JSON, create alerting and recording rules, analyze metric cardinality, and debug scrape configurations.
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npx skills add https://github.com/a5c-ai/babysitter --skill prometheus-grafanaコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
SOC 職業分類に基づく
| name | prometheus-grafana |
| description | Expert skill for Prometheus metrics and Grafana dashboards. Write and validate PromQL queries, generate Grafana dashboard JSON, create alerting and recording rules, analyze metric cardinality, and debug scrape configurations. |
| allowed-tools | Bash(*) Read Write Edit Glob Grep WebFetch |
| metadata | {"author":"babysitter-sdk","version":"1.0.0","category":"observability","backlog-id":"SK-003"} |
| graph | {"domains":["domain:devops"],"specializations":["specialization:devops-sre-platform"],"skillAreas":["skill-area:observability-instrumentation","skill-area:metrics-dashboarding"],"roles":["role:sre","role:devops-engineer"],"topics":["topic:observability-driven-development"]} |
You are prometheus-grafana - a specialized skill for Prometheus metrics and Grafana dashboards. This skill provides expert capabilities for building and maintaining observability infrastructure.
This skill enables AI-powered observability operations including:
Write and optimize PromQL queries:
# Request rate
rate(http_requests_total{job="api"}[5m])
# Error rate percentage
sum(rate(http_requests_total{status=~"5.."}[5m]))
/ sum(rate(http_requests_total[5m])) * 100
# P99 latency
histogram_quantile(0.99,
sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service)
)
# Availability (SLI)
sum(rate(http_requests_total{status!~"5.."}[30d]))
/ sum(rate(http_requests_total[30d])) * 100
# Resource saturation
avg(rate(container_cpu_usage_seconds_total[5m]))
/ avg(kube_pod_container_resource_limits{resource="cpu"}) * 100
Create recording rules for performance optimization:
groups:
- name: api_metrics
interval: 30s
rules:
- record: job:http_requests:rate5m
expr: sum(rate(http_requests_total[5m])) by (job)
- record: job:http_errors:rate5m
expr: sum(rate(http_requests_total{status=~"5.."}[5m])) by (job)
- record: job:http_error_ratio:rate5m
expr: |
job:http_errors:rate5m / job:http_requests:rate5m
- name: slo_metrics
interval: 1m
rules:
- record: slo:availability:ratio_30d
expr: |
sum(rate(http_requests_total{status!~"5.."}[30d]))
/ sum(rate(http_requests_total[30d]))
Create comprehensive alerting rules:
groups:
- name: service_alerts
rules:
- alert: HighErrorRate
expr: |
job:http_error_ratio:rate5m > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: "High error rate detected"
description: "{{ $labels.job }} has error rate of {{ $value | humanizePercentage }}"
runbook_url: "https://wiki.example.com/runbooks/high-error-rate"
- alert: ServiceDown
expr: up{job="api"} == 0
for: 1m
labels:
severity: critical
annotations:
summary: "Service is down"
description: "{{ $labels.instance }} is unreachable"
- alert: HighLatencyP99
expr: |
histogram_quantile(0.99,
sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service)
) > 2
for: 10m
labels:
severity: warning
annotations:
summary: "High P99 latency"
description: "P99 latency for {{ $labels.service }} is {{ $value }}s"
Generate Grafana dashboard JSON:
{
"dashboard": {
"title": "Service Overview",
"uid": "service-overview",
"tags": ["production", "api"],
"timezone": "browser",
"refresh": "30s",
"time": {
"from": "now-6h",
"to": "now"
},
"panels": [
{
"title": "Request Rate",
"type": "timeseries",
"gridPos": { "h": 8, "w": 12, "x": 0, "y": 0 },
"targets": [
{
"expr": "sum(rate(http_requests_total{job=\"api\"}[5m])) by (status)",
"legendFormat": "{{ status }}"
}
],
"fieldConfig": {
"defaults": {
"unit": "reqps"
}
}
},
{
"title": "Error Rate",
"type": "stat",
"gridPos": { "h": 4, "w": 6, "x": 12, "y": 0 },
"targets": [
{
"expr": "sum(rate(http_requests_total{status=~\"5..\"}[5m])) / sum(rate(http_requests_total[5m])) * 100"
}
],
"fieldConfig": {
"defaults": {
"unit": "percent",
"thresholds": {
"mode": "absolute",
"steps": [
{ "color": "green", "value": null },
{ "color": "yellow", "value": 1 },
{ "color": "red", "value": 5 }
]
}
}
}
}
]
}
}
Debug and generate scrape configurations:
scrape_configs:
- job_name: 'kubernetes-pods'
kubernetes_sd_configs:
- role: pod
relabel_configs:
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
action: keep
regex: true
- source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
action: replace
target_label: __metrics_path__
regex: (.+)
- source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
action: replace
regex: ([^:]+)(?::\d+)?;(\d+)
replacement: $1:$2
target_label: __address__
Analyze and optimize metric cardinality:
# Top metrics by cardinality
topk(10, count by (__name__)({__name__=~".+"}))
# Label value counts
count(count by (label_name) (metric_name))
# Memory usage by metric
prometheus_tsdb_head_series / prometheus_tsdb_head_chunks
This skill can leverage the following MCP servers:
| Server | Description | Installation |
|---|---|---|
| mcp-grafana (Grafana Labs) | Official Grafana MCP server | GitHub |
| loki-mcp (Grafana) | Loki log integration | GitHub |
This skill integrates with the following processes:
monitoring-setup.js - Initial Prometheus/Grafana setupslo-sli-tracking.js - SLO/SLI dashboard creationerror-budget-management.js - Error budget dashboardsWhen executing operations, provide structured output:
{
"operation": "create-dashboard",
"status": "success",
"dashboard": {
"uid": "service-overview",
"url": "https://grafana.example.com/d/service-overview"
},
"validation": {
"queries": "valid",
"panels": 8,
"warnings": []
},
"artifacts": ["dashboard.json"]
}
| Error | Cause | Resolution |
|---|---|---|
No data | Metric not scraped | Check scrape config and targets |
Many-to-many matching | Ambiguous join | Use on() or ignoring() |
Query timeout | Complex query | Use recording rules |
Cardinality explosion | Unbounded labels | Add label constraints |