How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/grafana/skills --skill promqlBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
PromQL Query Patterns
Docs: https://prometheus.io/docs/prometheus/latest/querying/basics/
PromQL returns either an instant vector, a range vector, or a scalar.
Golden rule: rate() / increase() require a range vector ≥ 4× the scrape interval. 60s scrape → use [5m] minimum.
Prerequisites
- A Prometheus / Mimir / Grafana Cloud endpoint to query (
/api/v1/queryor via Grafana Explore) - The PromQL pattern library in `references/patterns.md`
Common Workflows
1. Write + validate a query
bash
# 0. Point at your Prometheus/Mimir. For Grafana Cloud, use the metrics endpoint
# and add basic auth (-u "<metrics_user>:<token>") to each curl below.
PROM=http://localhost:9090 # or https://prometheus-prod-XX.grafana.net/api/prom
# 1. Sketch the query — for "5xx error rate per service":
EXPR='sum(rate(http_requests_total{status_code=~"5.."}[5m])) by (service)'
# 2. Validate syntax + that the metric/labels exist
curl -sG --data-urlencode "query=${EXPR}" \
"$PROM/api/v1/query" | jq '.status, (.data.result|length)'
# Expect: "success" and result count > 0. If 0 — check label spelling and scrape activity:
curl -sG --data-urlencode "match[]=http_requests_total" "$PROM/api/v1/series" | jq '.data | length'
# 3. Sanity-check the magnitude — open Grafana Explore, paste the expr,
# confirm the values look right against a known ground truth (k6 run, log count, etc.)2. Common patterns to copy
Per-status request rate (aggregate AFTER rate):
promql
sum(rate(http_requests_total{job="api"}[5m])) by (status_code)p95 latency (must keep le in the inner aggregation):
promql
histogram_quantile(0.95,
sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))Error rate with divide-by-zero guard:
promql
sum(rate(http_requests_total{status_code=~"5.."}[5m]))
/ (sum(rate(http_requests_total[5m])) > 0)Full library (recording rules, SLO burn-rate, offsets, cardinality hunt, native histograms): `references/patterns.md`.
3. Convert a slow dashboard query into a recording rule
yaml
# 1. Pick the slow expression, give it a recording-rule name
groups:
- name: http_request_rates
interval: 1m
rules:
- record: job:http_request_duration_p95:rate5m
expr: |
histogram_quantile(0.95,
sum(rate(http_request_duration_seconds_bucket[5m])) by (le, job))bash
# 2. After rules load, verify the new metric exists
curl -sG --data-urlencode "query=job:http_request_duration_p95:rate5m" \
"$PROM/api/v1/query" | jq '.data.result | length' # → > 0
# 3. Verify it matches the original expression for at least one sample window
# (Both queries should produce the same value at the same timestamp.)
# 4. Replace the dashboard panel expression with the recording-rule metric.Common bugs
histogram_quantilereturns NaN → forgotby (le)in the inner aggregation- "No data" → check the metric exists (
/api/v1/series) and the window ≥ 4× scrape interval - Wrong rate magnitude → counter was aggregated before
rate()(alwaysrate()first) - Query timeout → series count too high; use
topk(...)+ a recording rule + drop high-cardinality labels (see `references/patterns.md`)
