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Use when building Grafana dashboards backed by Prometheus, Loki, or Tempo, designing PromQL/LogQL queries, wiring template variables, setting alert rules, building SLO dashboards, or maintaining dashboards as code. Triggers: rate() vs increase() confusion, irate vs rate, label_replace, recording rules, alerting rule expressions, multi-dimensional template variables, ad-hoc filters, dashboard JSON model, provisioning via Terraform/grafonnet, p99 / histogram_quantile usage. NOT for Datadog/New Relic dashboards (vendor-specific), Grafana plugin development, or Loki ingestion pipeline tuning.
metadata
{"category":"DevOps & Infrastructure","tags":["grafana","prometheus","promql","dashboards","slo","observability"],"provenance":{"kind":"first-party","owners":["port-daddy"]},"pairs-with":[{"skill":"monitoring-stack-deployer","reason":"Deploys the Prometheus/Loki/Grafana stack these dashboards run on; this skill designs what renders inside it"},{"skill":"logging-observability","reason":"The producer-side log schema and levels that LogQL panels and error-rate queries depend on"},{"skill":"observability-apm-expert","reason":"Chooses the metrics, SLIs, and instrumentation that dashboards and alert rules visualize"}],"io-contract":{"kind":"deliverable","consumes":["[Truncated]","[Truncated]"],"produces":["[Truncated]","[Truncated]"]}}
Grafana Dashboard Builder
A good dashboard answers one question per panel and one big question per dashboard. PromQL is more expressive than most engineers use; the recurring traps are rate() vs increase(), label cardinality, and histogram quantile math.
A dashboard exists but is unreadable — too many panels, too many series.
SLO dashboards (latency p99, error budget burn).
Alert expressions that fire correctly without paging on transient blips.
Dashboards-as-code: provisioning via Terraform or grafonnet.
Core capabilities
PromQL essentials
# Per-second request rate over 5min window.
rate(http_requests_total[5m])
# Total requests over 5min.
increase(http_requests_total[5m])
# By status code.
sum by (status) (rate(http_requests_total[5m]))
# Error rate (ratio).
sum(rate(http_requests_total{status=~"5.."}[5m]))
/
sum(rate(http_requests_total[5m]))
# Latency p99 from a histogram metric.
histogram_quantile(0.99,
sum by (le) (rate(http_request_duration_seconds_bucket[5m]))
)
rate vs increase vs irate
Function
Returns
Use for
rate(m[5m])
Avg per-second rate over window
Most graphs and alerts. Smooth.
irate(m[5m])
Instantaneous rate from last 2 samples
Sparkline-style live views. Spiky.
increase(m[5m])
Total delta over window
"How many requests in 5min." Same as rate * window_seconds.
For alerts, prefer rate over irate — irate over a noisy counter triggers on every blip.
Histograms and le
Histogram metrics emit _bucket{le="..."}, _sum, _count. To compute quantiles:
histogram_quantile(0.99,
sum by (le, route) (rate(http_request_duration_seconds_bucket[5m]))
)
Aggregate by le AND any dimensions you want to keep in the result. Forgetting le returns NaN.
For p99 of all requests across routes:
histogram_quantile(0.99, sum by (le) (rate(http_request_duration_seconds_bucket[5m])))
label_replace and renaming
# Add a `service` label derived from `job`.
label_replace(up, "service", "$1", "job", "(.+)")
# Drop high-cardinality labels for graphing.
sum without (instance, pod) (rate(http_requests_total[5m]))
without is the cleaner inverse of by — sums everything except the listed labels.
Recording rules
For expensive queries used on many dashboards, pre-compute:
for: 10m is the dwell time — alert only fires after the condition is true for 10 contiguous minutes. Without it, every transient blip pages.
LogQL (Loki)
# Last 5 minutes of error logs.
{service="orders-api"} |= "level=error"
# Parse and filter on a JSON field.
{service="orders-api"} | json | status >= 500
# Rate of errors.
sum by (service) (rate({service="orders-api"} |= "level=error" [5m]))
# Latency from a structured field.
{service="orders-api"} | json | unwrap duration_ms | quantile_over_time(0.99, [5m])
Or grafonnet (Jsonnet) for templated dashboards across services. The point is: dashboards are reviewable, diff-able, and recoverable.
Annotations
Mark deploys, incidents, and feature flags on graphs:
# Annotation query — events from a Prometheus metric.
deployment_event{service="orders-api"}
Or use Grafana's annotation API to push events from CI.
Anti-patterns
rate() over a non-counter
Symptom: Negative rates, weird step changes.
Diagnosis:rate() only makes sense on monotonically-increasing counters. Applying to a gauge gives garbage.
Fix:delta() for gauges, rate() for counters. Use the right one.
irate in alerts
Symptom: Pager fires from a single noisy blip every few hours.
Diagnosis:irate reflects the last two samples; one bad sample triggers.
Fix:rate(...)[Nm] smoothed over minutes; combine with for: Xm.
Histogram quantile without by (le)
Symptom: Panel shows NaN.
Diagnosis:histogram_quantile needs the le label preserved through aggregation.
Fix:sum by (le, …) (rate(..._bucket[5m])).
Grafana variable that bloats panels
Symptom: "Include All" on a 5000-instance variable returns 5000 series.
Diagnosis: Multi-value variables with too-broad allowance.
Fix: Limit values, scope by another variable, or use regex to whittle. Aggregate before display.
Alert for: too short
Symptom: Pager fatigue from intermittent network hiccups.
Diagnosis:for: 1m fires on any blip.
Fix:for: 5m or for: 10m for SLO-tier alerts. Use for: 0 only for hard-failure metrics ("service down").
Dashboard with 30 panels
Symptom: Slow load, no one reads past the top row.
Diagnosis: "Add panel" reflex.
Fix: Divide into multiple focused dashboards: SLO, saturation, dependencies, debugging. Cross-link.
Quality gates
Every alert has a for: dwell time and a runbook URL.
PromQL queries reviewed for rate vs increase vs irate correctness.
Histogram quantiles always aggregate by le.
Recording rules used for queries that appear on >2 panels.
Template variables scoped (not multi-select-everything by default).
Dashboards stored as JSON in version control; provisioned, not edited live.
SLO dashboard has burn-rate and error-budget panels.
Annotations for deploys + incidents enabled.
Top row of every dashboard answers "is the system healthy" at a glance.
Deterministic Audit
Before shipping (or reviewing) a dashboard and its alerts, write the plan as JSON matching
schemas/grafana-dashboard-builder-plan.schema.json and run it through the deterministic
auditor:
auditGrafanaDashboardBuilder(plan) (in scripts/grafana_dashboard_builder_audit.mjs)
turns this skill's recurring traps into machine-checkable rules over structured fields — no
keyword matching: rate/irate/increase on a gauge (negative-rate garbage),
histogram_quantile without by (le) (the NaN panel), irate inside alert expressions,
SLO alerts with a dwell under 5 minutes, alerts with no runbook URL, expensive queries
shared by >2 panels without a recording rule, panel sprawl past 30, live-edited dashboards
outside version control, and Include-All on a high-cardinality variable. It returns
{ pass, score, findings, recommendations } and exits 1 on failure.
examples/sample-input.json is a well-formed SLO-dashboard plan (pass: true, zero
findings). See CHANGELOG.md for the bundle's history.
NOT for
Datadog / New Relic / Honeycomb dashboards — vendor-specific. No dedicated skill yet.
Grafana plugin development — separate domain. No dedicated skill.
Loki ingestion pipeline tuning — different concern. → structured-logging-design for the producer-side schema.
Distributed tracing dashboards (Tempo) — overlapping but distinct. → opentelemetry-instrumentation for span/trace generation.
Designing the metrics being measured — this skill assumes metrics exist. → opentelemetry-instrumentation for instrumentation patterns.
