adaptive-metrics

Generate adaptive aggregation rules to reduce Active Series in Grafana Cloud Metrics.

213|18|Updated Mar 17, 2026
One-click install
npx skills add https://github.com/grafana/skills --skill adaptive-metrics
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: adaptive-metrics
Source: https://github.com/grafana/skills/tree/main/skills/grafana-cloud/adaptive-metrics
Command: npx skills add https://github.com/grafana/skills --skill adaptive-metrics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Adaptive Metrics helps reduce Grafana Cloud billing and Prometheus storage by lowering Active Series counts through automated aggregation and label-dropping rules. It addresses high-cardinality metrics that inflate series counts but are not necessary for queries, dashboards, or alerts.

Core Features & Use Cases

  • Automatic recommendations: Analyze query history to identify labels that are never queried and propose aggregation rules that drop those labels.
  • Rule management: Create, review, and apply aggregation rules (exact or regex matches) via the UI or API to pre-aggregate high-cardinality metrics.
  • Cost and safety analysis: Estimate projected series reduction and billing impact, and provide guidance for confirming dashboard, alert, and recording-rule usage before dropping metrics.
  • Use case: An SRE team reduces Active Series for a noisy service by applying aggregated rules that drop pod-specific labels, cutting billable series without affecting PromQL dashboards.

Quick Start

Open Grafana Cloud Adaptive Metrics, run a recommendations scan for your desired lookback period, review the top suggestions, and apply the aggregation rules that safely drop unused labels.

Frequently Asked Questions about adaptive-metrics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce high-cardinality Prometheus metrics costs in Grafana Cloud?

Reduce Grafana Cloud metrics costs by analyzing query history to identify unused labels on high-cardinality metrics, then generating adaptive aggregation rules that safely drop those labels to lower Active Series and billing.

What is the best way to identify unused labels driving up Prometheus storage?

Identify unused labels by running a recommendations scan over a specific lookback period, which analyzes your query history to detect labels that are never queried across dashboards, alerts, and recording rules.

Can I create aggregation rules for high-cardinality metrics without breaking existing dashboards?

Create safe aggregation rules by confirming dashboard, alert, and recording-rule usage before applying them, ensuring that dropped labels or metrics do not impact active PromQL queries.

How do I estimate series reduction and billing impact before dropping metrics?

Estimate projected series reduction and billing impact by reviewing the cost and safety analysis generated for each recommendation, which calculates the expected Active Series decrease before you apply rules.

Does Adaptive Metrics support applying aggregation rules via API and UI?

Adaptive Metrics supports applying aggregation rules through both the Adaptive Metrics UI and API, allowing you to create, review, and apply exact or regex match rules across remote_write pipelines.