sre-dashboards

name	sre-dashboards
description	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.
license	MIT
metadata	{"author":"devops-skills","version":"1.0"}

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

Related Skills

• prometheus-grafana - Dashboard implementation and PromQL
• opentelemetry - Standardized telemetry instrumentation
• alerting-oncall - Reliability alert routing and escalation
• agent-observability - AI workload reliability telemetry