skills/ bagelhole/devops-security-agent-skills

sre-dashboards

Design and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.

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SRE Dashboards

Build dashboards that help teams detect, triage, and prevent reliability incidents.

When to Use This Skill

Use this skill when:

  • Defining service-level dashboards for production systems
  • Tracking SLO health and error-budget burn
  • Creating incident command-center views
  • Standardizing dashboard patterns across teams

Prerequisites

  • Metrics pipeline (Prometheus, OpenTelemetry, or vendor equivalent)
  • Logs/traces linked to services and environments
  • Agreed service taxonomy (team, service, tier, environment)

Dashboard Architecture

Structure dashboards in layers:

  1. Executive Reliability View: SLO attainment, incident counts, MTTR trends.
  2. Service Health View: RED/USE metrics, dependency health, release markers.
  3. Deep-Dive View: Per-endpoint latency, resource saturation, error categories.

Keep each view answer-oriented:

  • Are customers impacted?
  • What changed?
  • Where is the bottleneck?

Core SRE Panels

Golden Signals

  • Latency: p50/p95/p99 request duration by endpoint
  • Traffic: request throughput and queue depth
  • Errors: 5xx rate, failed jobs, timeout ratio
  • Saturation: CPU, memory, disk I/O, thread/connection pool exhaustion

SLO Panels

  • Current SLI value (rolling windows: 5m, 1h, 24h, 30d)
  • Error-budget remaining (%)
  • Burn-rate panels (fast and slow windows)
  • Multi-window burn alert status

Change Correlation

  • Deployment markers and config-change annotations
  • Feature flag state overlays
  • Upstream/downstream dependency error rates

Example PromQL Snippets

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

# p95 latency by route
histogram_quantile(0.95,
  sum by (le, route) (rate(http_request_duration_seconds_bucket[5m]))
)

# Fast burn rate (5m / 1h)
(
  sum(rate(http_requests_total{status=~"5.."}[5m]))
  / sum(rate(http_requests_total[5m]))
)
/
(
  sum(rate(http_requests_total{status=~"5.."}[1h]))
  / sum(rate(http_requests_total[1h]))
)

Operational Guidelines

  • Use consistent color semantics (green=healthy, yellow=degrading, red=breach)
  • Label units explicitly (ms, req/s, %, cores)
  • Default time windows to incident-friendly ranges (15m, 1h, 6h, 24h)
  • Minimize panel count per dashboard to reduce cognitive load
  • Add runbook links directly in panel descriptions

Troubleshooting

Panel appears flat or empty

  • Verify label cardinality and filters (service, env, region)
  • Confirm scrape/ingest latency is within expected range
  • Check metric rename regressions after instrumentation updates

High cardinality slows dashboards

  • Aggregate by stable dimensions (service, route_group) instead of raw IDs
  • Use recording rules for expensive percentile and ratio queries
  • Split deep-dive dashboards from NOC summary dashboards

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