aggregating-gauge-metrics

Consolidate pre-aggregated Observe metrics into summaries or time-series using OPAL.

Updated Aug 27, 2026

Search Tags:#dashboard #time-series #aggregate #OPAL #gauge-metrics #metrics-analysis

Ready to run

npx skills add https://github.com/rustomax/observe-community-mcp --skill aggregating-gauge-metrics

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aggregating-gauge-metrics
Source: https://github.com/rustomax/observe-community-mcp/tree/main/skills/aggregating-gauge-metrics
Command: npx skills add https://github.com/rustomax/observe-community-mcp --skill aggregating-gauge-metrics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams rapidly analyze and summarize pre-computed metrics stored in Observe using OPAL, enabling faster insights without querying raw events.

Core Features & Use Cases

Pattern-based querying: use align, m(), and aggregate to compute summaries or time-series from gauge, counter, and delta metrics.
Time-series and dashboards: produce per-service totals and trends for dashboards and reports.
Educational guidance: includes best practices for metric discovery, validation, and dimension usage.

Quick Start

Use the aggregating-gauge-metrics skill to compute total requests from the span_call_count_5m metric over the last hour using align and aggregate.

Frequently Asked Questions about aggregating-gauge-metrics

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

FAQPage Schema

How do I aggregate pre-computed gauge metrics in Observe using OPAL?▼

To aggregate gauge metrics in Observe, use OPAL patterns like align, m(), and aggregate to consolidate pre-computed gauge, counter, and delta metrics into concise summaries across configurable time windows.

What is the best way to summarize time-series metrics for per-service dashboards?▼

Summarizing time-series metrics for dashboards is done by applying aggregate functions like sum, avg, max, or min to pre-computed Observe metrics, producing either a summary with bins: 1 or a time-series output for per-service trends.

Do I need to know OPAL align and m() functions to compute metric totals?▼

Yes, computing metric totals requires knowledge of OPAL align, m(), and aggregate patterns to properly query and consolidate pre-aggregated Observe metrics like counters and gauges into accurate per-service totals.

Can I compute averages and trends from delta metrics over a specific time window?▼

Yes, you can compute averages and trends from delta metrics by using OPAL aggregate functions across configurable time windows, allowing you to produce time-series outputs that track changes over the specified period.

Does aggregating pre-computed metrics require querying raw events in Observe?▼

No, aggregating pre-computed metrics does not require querying raw events because the Skill operates directly on pre-aggregated Observe metrics, enabling faster insights by bypassing raw event processing entirely.