Promql

What it does

The instructions cover query shape, counter rates, histogram aggregation, error ratios and the move from a dashboard expression to a recording rule. They pair expression writing with endpoint checks and a comparison against a known workload. A separate reference file contains the wider pattern library.

Best for

Engineers already using Prometheus, Mimir or Grafana metrics who need to explain a chart, debug missing data or review an expensive expression. Access to the relevant metrics endpoint and knowledge of the real labels are essential.

Tradeoffs and requirements

A syntactically accepted expression can still measure the wrong thing. Scrape intervals, missing series, counter resets and label cardinality change interpretation. Querying production telemetry can expose operational information or add load, so use bounded requests and appropriate access. This assessment did not contact a metrics service or benchmark the suggested queries.

Unknown; inspect source requirements and supporting scripts.

Install from the source

First time installing a skill? Follow the walkthrough →

Inspect the pinned source snapshot and its supporting files first. The command selects this skill through the open-source skills CLI. Node.js and agent-specific setup may be required. Installation can download code. Catalog ingestion never executes skill instructions or install scripts.

npx skills add https://github.com/grafana/skills/tree/8fb72a527961f05e708f53d2a0500709e12d7970/skills/grafana-core/promql --skill promql

Review pinned skill folder ↗ · CLI documentation ↗

After source review, use npx skills check and npx skills update to inspect and apply upstream updates. Re-check changed instructions before running them.

Safety & review signals

Static review only. This is not a security audit or a guarantee of safety.

SKILL.md text only; scripts, dependencies and runtime behavior not audited.

• Network Access — review the source context.
• Shell Or Package Install — review the source context.

Allowed tools declaration: Unknown. Runtime permissions and network behavior: Unknown.

Offline package integrity

A temporary folder-copy check, not an agent installation or runtime safety verdict.

2 files copied and checksummed from this exact source snapshot. No package code or installation scripts were executed.

Inventory SHA-256: 06e3e080dd4a0ad64116f21fca16d7cbcca5d0e9bb7015d9a4fbf0d73e23c14b

AI Craft Pad Skill Score

85/100 · Metadata coverage 100% · Model 1.0

Scores summarize observable discovery signals. Popularity and repository activity apply to the whole repository. Unknown runtime safety earns no certification.

Documentation

100/100

100

Maintenance

100/100

100

License Clarity

100/100

100

Repository Health

100/100

100

Compatibility Evidence

80/100

80

Popularity

45/100

45

Originality

80/100

80

Static Safety

60/100

60

Supporting-file static inspection

Bounded source inspection, not a runtime security audit. This inspection does not execute code.

0 text files inspected at this exact repository snapshot. 1 files outside the inspection budget or type allowlist. Dependencies were not installed or independently audited.

Absence of a pattern is not proof of safety. Permission needs and runtime behavior remain Unknown.

Suggested workflow

Identify metric type, labels, units and observation window, validate a narrow expression, then compare its magnitude with an independent event count or controlled test. Before replacing an expression with a recording rule, compare both at matching timestamps.

How to judge the result

Check expected and missing-data cases, aggregation boundaries and histogram assumptions. Keep the query and data window alongside the result. A nonempty result is not proof of correct SLO math or an accurate performance diagnosis.