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NikoxkxNikoxkx/Agent-Skills/skills/devops/monitoring-dashboard/SKILL.md

monitoring-dashboard

Skill1 starsChanged 5 months agoApache-2.0View on GitHub

Creates monitoring dashboards and alerting rules for production services. Use when setting up observability with Prometheus, Grafana, Datadog, or similar.

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Always link to a runbook in the alert annotation.

    line 55
  2. Distinctive:

    Noisy alerts: Use error budget burn rate instead of raw thresholds.

    line 74

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Overview

Builds production monitoring stacks centered on the four golden signals (latency, traffic, errors, saturation). Includes Prometheus scrape config, useful PromQL queries, Grafana dashboard JSON or Terraform, alert rules with runbook links, SLO/SLI definitions, and on-call best practices.

When to Use This Skill

  • Adding observability to a new or existing service.
  • Replacing or improving existing dashboards that are noisy or missing key signals.
  • Defining SLIs/SLOs for a team or product.

Prerequisites

  • Metrics exposed by the application (Prometheus client library, OpenTelemetry, StatsD, etc.).
  • Prometheus or compatible scraper running (or Datadog, New Relic, etc.).
  • Grafana or equivalent visualization.

Steps

  1. Define SLIs (Service Level Indicators) for the service:
    • Availability: successful requests / total.
    • Latency: p95 or p99 of request duration.
    • Error rate.
    • Saturation (CPU, memory, queue depth).
  2. Instrument the code (if not done):
    • Request duration histogram.
    • Counter for errors by type.
    • Gauges for queue depth, active connections.
  3. Prometheus scrape & recording rules:
    • Scrape config for the app + node-exporter + cAdvisor (if k8s).
    • Recording rules for common aggregates (e.g., job:request_duration_seconds:rate5m).
  4. Grafana dashboard:
    • One row per golden signal.
    • Heatmaps for latency distribution.
    • Error budget burn rate.
    • Top endpoints by latency/error.
  5. Alerting rules:
    • Page on critical (error budget burning fast).
    • Ticket on warning.
    • Always link to a runbook in the alert annotation.
  6. SLOs:
    • Example: 99.9% availability over 28 days.
    • Error budget calculation and visualization.
  7. Output:
    • prometheus.yml snippet.
    • Recording + alerting rules YAML.
    • Grafana dashboard JSON (importable).
    • Runbook template (markdown).
    • On-call rotation recommendations.

Examples

A complete set of recording/alerting rules for a typical web service, a Grafana dashboard JSON focused on the four golden signals, and an SLO error budget dashboard are included.

Edge Cases & Error Handling

  • Noisy alerts: Use error budget burn rate instead of raw thresholds.
  • Missing metrics: Provide a checklist of must-have metrics per service type.
  • Multi-tenant / high cardinality: Careful with label usage.

Verification

  1. Metrics appear in Prometheus.
  2. Grafana dashboard loads and shows live data.
  3. A test alert fires and creates a ticket with runbook link.
  4. Error budget dashboard correctly shows remaining budget.
  5. Success: Team has clear visibility into service health and actionable alerts.

References

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