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timescaledb-data

Model TimescaleDB hypertables, compression, retention, and continuous aggregates for crypto market data.

Updated Feb 1, 2026
Search Tags:#sql#time-series#data-modeling#timescaledb#jdbc#data-persistence#hypertable
One-click install
npx skills add https://github.com/akarazhev/crypto-scout --skill timescaledb-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: timescaledb-data
Source: https://github.com/akarazhev/crypto-scout/tree/main/.opencode/skills/timescaledb-data
Command: npx skills add https://github.com/akarazhev/crypto-scout --skill timescaledb-data

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

TimescaleDB data modeling, schema design, and operations guidance for cryptocurrency market time-series data within the crypto-scout ecosystem.

Core Features & Use Cases

  • Hypertable design and data modeling to support crypto exchange data (Bybit/CMC) and long-term storage.
  • Storage optimization through compression, retention, and reorder policies to sustain performance and cost.
  • End-to-end data workflows including repository patterns, exact-once processing, and continuous aggregates.

Quick Start

Create a hypertable for crypto_scout.bybit_spot_kline_1m using time as the partition key and enable a 7-day compression policy.

Frequently Asked Questions about timescaledb-data

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

FAQPage Schema
How do I model time-series data in TimescaleDB for crypto market workloads?▼

Modeling time-series data in TimescaleDB for crypto markets involves designing hypertables using time as the partition key to optimize storage and queries for exchange data. It supports long-term storage workflows across sources like Bybit and CoinMarketCap.

What is the best way to configure compression and retention policies for hypertables?▼

Configuring compression and retention policies for hypertables requires applying storage optimization rules, such as a 7-day compression policy, to sustain query performance and control costs for high-frequency crypto time-series data.

How do continuous aggregates improve query performance for cryptocurrency time-series data?▼

Continuous aggregates improve cryptocurrency time-series query performance by automatically pre-computing and materializing summarized data, reducing the computational load during real-time analytics over large hypertables.

Can I use JDBC integration with TimescaleDB for exactly-once data processing?▼

Yes, JDBC integration with TimescaleDB supports exactly-once data processing by implementing repository patterns and offset management to ensure reliable, duplicate-free data ingestion from crypto exchange sources.

Does this time-series data modeling approach support both Bybit and CoinMarketCap data sources?▼

Yes, this time-series data modeling approach explicitly supports Bybit and CoinMarketCap data sources by defining repository-based data flows and hypertable schemas tailored to handle their specific market data structures.

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