Grafana promql

Skill Jylhis/skills/skills/services/grafana-promql

Curated Agent Skills marketplace for Claude Code, Codex, and Google Antigravity.

Install

npx -y skills add Jylhis/skills --skill grafana-promql

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.6 KB, 979 tokens by cl100k_base, as published. Nobody here has run it

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

Common Workflows

1. Write + validate a query

# 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):

sum(rate(http_requests_total{job="api"}[5m])) by (status_code)

p95 latency (must keep le in the inner aggregation):

histogram_quantile(0.95,
  sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))

Error rate with divide-by-zero guard:

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

# 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))

# 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_quantile returns NaN → forgot by (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() (always rate() first)
Query timeout → series count too high; use topk(...) + a recording rule + drop high-cardinality labels (see references/patterns.md)