Como usar
- Copie o comando e verifique o pacote antes de instalar no seu ambiente de agente.
- Consulte no original as ferramentas, credenciais e ambientes necessários.
- Descreva os dados de entrada e a entrega esperada; confira a saída com as fontes.
npx skills add https://github.com/grafana/skills --skill promqlAntes de começar
- A instalação não inclui assinatura de modelo, franquia API ou acesso aos seus arquivos.
- Estrelas e instalações indicam atividade, não um teste independente de qualidade ou segurança.
Documento original do Skill
Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.
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`)
