stream-chain

Name: stream-chain
Availability: InStock
Author: attentiondotnet

Coordinate multi-agent prompts into sequential streaming data workflows.

19|1|Updated Mar 9, 2026

Search Tags:#workflow #multi-agent #streaming #orchestration #memory #pipeline

Ready to run

npx skills add https://github.com/attentiondotnet/Ruview --skill stream-chain-attentiondotnet

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/attentiondotnet/Ruview/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/attentiondotnet/Ruview --skill stream-chain-attentiondotnet

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Stream-Chain solves the challenge of coordinating multiple AI agents and steps in a streaming, sequential workflow by enabling outputs to flow from one stage to the next in real-time.

Core Features & Use Cases

Custom Chains (run): Build bespoke prompt sequences where each step consumes the previous output, enabling tailored multi-agent collaboration.
Predefined Pipelines (pipeline): Use battle-tested workflows for common tasks to accelerate development and ensure consistency.
Memory & Orchestration: Propagates context between steps and supports optional memory to persist state across runs.

Quick Start

Create a three-step chain with run to process sample data.

Frequently Asked Questions about stream-chain

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

FAQPage Schema

How do I orchestrate multi-agent workflows with sequential data processing?▼

You can orchestrate multi-agent workflows by coordinating prompts to form sequential, streaming data pipelines. This approach applies stepwise transformations across code, docs, and analyses, allowing outputs to flow from one stage to the next in real-time.

What is the best way to build custom multi-agent chains for AI task orchestration?▼

Building custom multi-agent chains involves creating bespoke prompt sequences where each step consumes the previous output. This enables tailored multi-agent collaboration for complex data processing and stepwise transformations.

Can I use predefined pipelines for common multi-agent orchestration tasks?▼

Yes, you can use predefined pipelines for common multi-agent tasks to accelerate development and ensure consistency. These battle-tested workflows provide a structured approach to streaming data processing without needing custom configuration.

Does multi-agent orchestration support context propagation and memory across pipeline runs?▼

Multi-agent orchestration supports context propagation between steps and includes optional memory to persist state across runs. This ensures sequential streaming workflows maintain necessary context for complex transformations.

When should I use sequential streaming workflows for AI task orchestration?▼

Use sequential streaming workflows when processing complex data that requires stepwise transformations across code, docs, or analyses. It is ideal when outputs must flow from one stage to the next in real-time for continuous data processing.