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promql

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Write, validate, and optimize PromQL for Prometheus / Grafana Mimir / Grafana Cloud Metrics. Covers `rate` vs `irate` vs `increase`, label matchers and regex, `sum / avg / topk / by / without` aggregation, classic + native `histogram_quantile`, ratios with divide-by-zero guards, `absent` / `changes` for staleness, time offsets and `predict_linear`, recording-rule naming, SLO + burn-rate math, and a cardinality-hunting playbook. Use when writing a metric query, fixing wrong p95s, building an error-budget alert, debugging "query is slow", finding the noisy label that blew up cardinality, or migrating a dashboard query to a recording rule — even when the user says "calculate the error rate", "p99 latency", "sum by service", "why is this query slow", or "what's filling Mimir" without naming PromQL.

skills/grafana-core/promql/SKILL.md

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Nov 10, 2026
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# 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/query` or via Grafana Explore)
- The PromQL pattern library in [`references/patterns.md`](references/patterns.md)

## Common Workflows
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