Is this skill useful for financial data?

Absolutely. It contains specialized implementation details for KDB+/q and QuestDB, focusing on ASOF joins and high-frequency tick data processing.

Which databases are supported by this skill?

It covers 14 databases including InfluxDB (Flux/3.0), Prometheus, TimescaleDB, QuestDB, VictoriaMetrics, TDengine, KDB+, and AWS Timestream.

Can it help with data retention and downsampling?

Yes, it includes patterns for designing raw-to-aggregate tiers (1m, 5m, 1h) and implementing automated retention policies for various engines.

How does this skill handle high tag cardinality?

It provides specific bounding rules and design patterns to identify and limit high-cardinality tags before they degrade database performance.

Time-Series Database Specialist

Name: Time-Series Database Specialist
Author: rnavarych

byrnavarych

•

데이터베이스 관리

Provides expert operational guidance and implementation patterns for 14 major time-series databases, including InfluxDB, Prometheus, and TimescaleDB.

This skill equips Claude with deep technical knowledge to design, implement, and optimize high-performance time-series storage systems. It covers a comprehensive range of databases—from general-purpose options like InfluxDB and TimescaleDB to specialized engines like KDB+ for finance and TDengine for IoT. It provides actionable strategies for managing tag cardinality, designing multi-tier retention policies, and implementing efficient downsampling, ensuring your observability, financial, or IoT workloads remain scalable and cost-effective.

주요 기능

01Comparative selection matrix for matching workloads to the ideal database engine.

02Multi-tier retention and downsampling implementation patterns for efficient storage.

03Advanced cardinality management strategies to prevent performance bottlenecks.

04Comprehensive operational guides for 14+ TSDBs including InfluxDB 3.0, Prometheus, and VictoriaMetrics.

0511 GitHub stars

06Database-specific query optimization for Flux, PromQL, SQL, and q language.

사용 사례

01Designing financial data systems for high-frequency tick data and ASOF joins.

02Architecting scalable observability backends for Kubernetes and microservices environments.

03Building high-ingestion IoT pipelines for sensor data with optimized storage footprints.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add rnavarych/alpha-engineer time-series-databases

For use in Claude.ai and ChatGPT

주요 기능

01Comparative selection matrix for matching workloads to the ideal database engine.

02Multi-tier retention and downsampling implementation patterns for efficient storage.

03Advanced cardinality management strategies to prevent performance bottlenecks.

04Comprehensive operational guides for 14+ TSDBs including InfluxDB 3.0, Prometheus, and VictoriaMetrics.

0511 GitHub stars

06Database-specific query optimization for Flux, PromQL, SQL, and q language.

사용 사례

01Designing financial data systems for high-frequency tick data and ASOF joins.

02Architecting scalable observability backends for Kubernetes and microservices environments.

03Building high-ingestion IoT pipelines for sensor data with optimized storage footprints.