Timescaledb

Skill G1Joshi/Agent-Skills/skills/databases/timescaledb

A comprehensive skill catalog for AI agents

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npx -y skills add G1Joshi/Agent-Skills --skill timescaledb

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TimescaleDB PostgreSQL for time-series. Use for time-series on Postgres.

SKILL.md

1.8 KB, 384 tokens by cl100k_base, as published. Nobody here has run it

TimescaleDB

TimescaleDB is a time-series database built as an extension on top of PostgreSQL. It gives you the scale of NoSQL time-series with the reliability and tooling of Postgres.

When to Use

SQL familiarity: You want time-series but already know SQL and use Postgres drivers.
Relational + Time: You need to JOIN your sensor data (Time Series) with Device metadata (Relational Tables).
Compression: Highest-in-class compression (90%+) for historical data.

Quick Start

-- Convert standard table to hypertable
SELECT create_hypertable('conditions', 'time');

-- Query using standard SQL time-bucket functions
SELECT time_bucket('15 minutes', time) AS bucket,
       avg(temperature)
FROM conditions
GROUP BY bucket
ORDER BY bucket DESC;

Core Concepts

Hypertables

The abstraction layer. It looks like a single table, but effectively partitions data into chunks by time interval.

Continuous Aggregates

Real-time materialized views. "Keep a running average of temperature per hour". It updates incrementally.

Compression

Columnar compression on old chunks. Turns row-based Postgres pages into highly compressed columnar arrays.

Best Practices (2025)

Do:

Enable Compression: It improves query speed (less I/O) and saves massive disk space.
Use Tiered Storage: Keep recent hot data on SSD, move compressed old data to S3 (Bottomless storage in cloud).
Join tables: Use the power of Postgres to join your metrics with your business data.

Don't:

Don't update compressed chunks: Updating old, compressed data is slow (Copy-on-write). Design for append-only patterns.

References

TimescaleDB Documentation

Gives 0 of the 12 instructions most databases sql skills give in 384 tokens

Counted across 589 of the 662 authors here whose files we hold, read 2026-08-06

use parameterized queriesin 36 of 589, across 32 files
use timestamptz for timestampsin 30 of 589, across 12 files
create indexes concurrentlyin 29 of 589, across 23 files
index foreign keysin 28 of 589, across 17 files
use numeric type for moneyin 25 of 589, across 8 files
select only required columnsin 24 of 589, across 19 files
use cursor pagination instead of OFFSETin 23 of 589, across 15 files
add indexes manually on foreign key columnsin 22 of 589, across 11 files
read individual rule files for detailed explanationsin 18 of 589, across 4 files
configure connection poolingin 18 of 589, across 16 files
put equality columns before range columns in indexesin 17 of 589, across 9 files
normalize to third normal formin 17 of 589, across 8 files

Said here and by no other author read

use timescaledb for time-series data
join relational tables with time-series data
convert standard tables to hypertables
use time-bucket functions for queries
use continuous aggregates for running metrics
enable compression on historical data

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.