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.
来源信息
- 仓库
- BagelHole/DevOps-Security-Agent-Skills
- 最近来源活动
- 2026年5月22日 13:02
- 检测到的 SKILL.md 语言
- 英语
- 星标
- 1,154
- 分支
- 164
安装方式
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
检查来源文件
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
正在显示 SKILL.md
SKILL.md
来源说明 · 只读预览- 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
```promql
# API error rate (%)
100 * sum(rate(http_requests_total{status=~"5.."}[5m]))
/ sum(rate(http_requests_total[5m]))
```
```promql
# p95 latency by route
histogram_quantile(0.95,
sum by (le, route) (rate(http_request_duration_seconds_bucket[5m]))
)
```
```promql
# 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](../prometheus-grafana/) - Dashboard implementation and PromQL
- [opentelemetry](../opentelemetry/) - Standardized telemetry instrumentation
- [alerting-oncall](../alerting-oncall/) - Reliability alert routing and escalation
- [agent-observability](../../ai/agent-observability/) - AI workload reliability telemetry
在 GitHub 查看