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turbo-architecture

Guide Turbo pipeline architecture across sources, patterns, sizing, and deployment strategies.

8|Updated Feb 26, 2026
Search Tags:#architecture#patterns#data-flow#multi-chain#source-selection#turbo-pipelines#resource-sizing
One-click install
npx skills add https://github.com/goldsky-io/goldsky-agent --skill turbo-architecture
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: turbo-architecture
Source: https://github.com/goldsky-io/goldsky-agent/tree/main/skills/turbo-architecture
Command: npx skills add https://github.com/goldsky-io/goldsky-agent --skill turbo-architecture

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Designing Turbo pipelines involves selecting the right source types, data flow patterns, resource sizing, sink strategies, and multi-chain deployment approaches. This Skill provides structured guidance to make architecture decisions quickly and consistently across projects.

Core Features & Use Cases

  • Source selection guidance: choose between dataset and Kafka sources for historical backfill or real-time streams.
  • Pattern recommendations: linear, fan-in, fan-out, and multi-chain approaches with templates to start quickly.
  • Deployment strategy: resources sizing (s, m, l), streaming vs job mode, and per-chain templating for scalable, maintainable pipelines.
  • Dynamic table and sink guidance: design for runtime lookups and multiple sinks.

Quick Start

Describe your pipeline goals and I will guide you through choosing source types, data-flow patterns, and sink strategies.

Frequently Asked Questions about turbo-architecture

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

FAQPage Schema
How do I design data pipelines for multi-chain deployments?▼

Designing data pipelines for multi-chain deployments requires applying per-chain templating, selecting source types, and structuring data-flow patterns to maintain scalable and consistent pipeline architecture.

What is the best way to choose between Kafka and dataset sources for pipeline architecture?▼

Choosing between Kafka and dataset sources depends on data flow requirements: select Kafka for real-time streaming pipelines, and use dataset sources for historical backfill operations.

How do I size resources for streaming versus job-mode pipelines?▼

Sizing resources for streaming versus job-mode pipelines involves selecting resource profiles (s, m, l) based on your deployment strategy and the specific data-flow patterns applied.

When do I need dynamic tables in pipeline sink strategies?▼

You need dynamic tables in pipeline sink strategies when designing for runtime lookups, allowing your architecture to support multiple sinks and flexible data retrieval during execution.

Can I use fan-out patterns for single-source linear pipelines?▼

You can apply fan-out patterns to single-source linear pipelines by routing the source data to multiple sinks, though linear patterns are typically simpler for single-stream routing.

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