bagelhole/sre-dashboards
devops/observability/sre-dashboards/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add bagelhole/devops-security-agent-skills sre-dashboardsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
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Last scanned: May 23, 2026, 3:02 AM92.0s1 file scanned
SKILL.md
- 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
- 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:
- Executive Reliability View: SLO attainment, incident counts, MTTR trends.
- Service Health View: RED/USE metrics, dependency health, release markers.
- 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
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