timescaledb

Manage time-series data in TimescaleDB via hypertables, compression, and continuous aggregates.

2|1|Updated Mar 13, 2026
One-click install
npx skills add https://github.com/robomotionio/robomotion-skills --skill timescaledb-robomotionio
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: timescaledb
Source: https://github.com/robomotionio/robomotion-skills/tree/main/skills/timescaledb
Command: npx skills add https://github.com/robomotionio/robomotion-skills --skill timescaledb-robomotionio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

TimescaleDB provides scalable storage and fast analysis for high-volume time-series data by offering hypertables, time-based aggregations, and durable compression.

Core Features & Use Cases

  • Create hypertables and ingest time-stamped data at scale.
  • Run time_bucket based aggregations and continuous aggregates for rapid insights.
  • Optimize storage with compression policies and long-term retention.

Quick Start

Install the robomotion-timescaledb package, connect to your TimescaleDB instance, and begin managing hypertables and time-based queries.

Frequently Asked Questions about timescaledb

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

FAQPage Schema
What is the best way to manage time-series data in TimescaleDB at scale?

The best way to manage time-series data in TimescaleDB is by using hypertables, which automatically partition data by time. This enables fast ingestion of time-stamped records and efficient time-based queries for high-volume workloads.

How do I run fast time-based aggregations on high-volume time-series data?

You can run fast time-based aggregations by using the time_bucket function and creating continuous aggregates. This pre-calculates summarized data, providing rapid insights for IoT telemetry and financial tick data analysis.

Does TimescaleDB support automated data retention and storage compression?

Yes, TimescaleDB supports automated data retention and storage compression. You can apply compression policies to optimize storage usage and set long-term retention rules to manage how long historical time-series data is kept.

Can I use TimescaleDB for both infrastructure monitoring and financial tick data?

Yes, you can use TimescaleDB for infrastructure monitoring and financial tick data. Its hypertable architecture and time_bucket aggregations are designed for high-performance time-series analytics across these specific use cases.

Do I need specific credentials configured to connect and execute queries in TimescaleDB?

Yes, you need TimescaleDB credentials configured via a vault to connect and execute queries. You also need the robomotion CLI installed to manage hypertables, insert data, and apply compression policies.