Sre Dashboards Agent Skill
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
one command, takes just this skill from the repositorynpx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill sre-dashboardsThe instruction itself
14 sections, as written by the authorSRE 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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How to use it
Take bagelhole/sre-dashboards from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.