expanso-aggregate-time-windows

Performs time-window aggregations on metrics and analytics data using Expanso.

1|Updated Feb 1, 2026

Search Tags:#metrics #aggregation #data transformation #pipeline #expanso #time windows

Ready to run

npx skills add https://github.com/expanso-io/expanso-skills --skill expanso-aggregate-time-windows

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: expanso-aggregate-time-windows
Source: https://github.com/expanso-io/expanso-skills/tree/main/skills/recipes/aggregate-time-windows
Command: npx skills add https://github.com/expanso-io/expanso-skills --skill expanso-aggregate-time-windows

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the process of aggregating time-series data, enabling efficient analysis of metrics and events over defined time intervals.

Core Features & Use Cases

Time-Window Aggregations: Supports tumbling, sliding, and multi-level aggregation patterns.
Data Transformation: Cleans, validates, and transforms raw event data into aggregated insights.
Use Case: Analyze sensor data to calculate average, min, and max temperatures within 1-minute tumbling windows and 5-minute sliding windows, identifying trends and anomalies.

Quick Start

Run the pipeline with sample data using the provided script.

Frequently Asked Questions about expanso-aggregate-time-windows

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

FAQPage Schema

How do I aggregate time-series metrics using tumbling and sliding windows?▼

To aggregate time-series metrics, this Skill applies tumbling and sliding window patterns to calculate averages, min/max values, and data quality scores. It validates raw event data and transforms it into aggregated insights for trend analysis.

Can I use Expanso to calculate average and min/max temperatures from sensor events?▼

Yes, you can use Expanso to calculate average and min/max temperatures from sensor events. The pipeline groups event data into 1-minute tumbling or 5-minute sliding windows, enabling you to identify trends and anomalies in metrics.

What is the best way to validate input data for time-window aggregation?▼

The best way to validate input data for time-window aggregation is to run the provided script, which cleans and validates raw event data before processing. This ensures accurate multi-level aggregations and prevents malformed metrics from skewing results.

Does this time-series aggregation pipeline output results to HTTP endpoints?▼

Yes, the time-series aggregation pipeline outputs aggregated results directly to HTTP endpoints or files. This allows downstream systems to consume the calculated metrics, averages, and data quality scores seamlessly.

When do I need multi-level aggregation patterns for metrics data?▼

You need multi-level aggregation patterns for metrics data when analyzing complex time-series streams that require nested time intervals. This approach processes sensor events hierarchically to capture granular trends and broader anomalies simultaneously.