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MarcoDava
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stream-chain

Orchestrate multi-agent workflows by streaming outputs between sequential steps.

1|Updated Feb 7, 2026
Search Tags:#automation#workflow#multi-agent#streaming#orchestration#pipeline
One-click install
npx skills add https://github.com/MarcoDava/MockCortex --skill stream-chain-marcodava
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/MarcoDava/MockCortex/tree/main/.agents/skills/stream-chain
Command: npx skills add https://github.com/MarcoDava/MockCortex --skill stream-chain-marcodava

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinated multi-agent workflows can be complex to implement, requiring reliable data flow between steps and consistent context persistence across tasks.

Core Features & Use Cases

  • Orchestrates custom chains (run) and predefined pipelines (pipeline) to coordinate sequential steps with streaming data between agents.
  • Supports deterministic task execution, memory of prior outputs, and flexible configuration for complex automation scenarios.
  • Use cases include data processing pipelines, code review automation, and research task orchestration in multi-agent environments.

Quick Start

Provide a three-step stream-chain to analyze a dataset, transform it, and generate a summary.

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 streaming data between sequential steps?▼

Multi-agent workflows can be orchestrated by streaming outputs from one step directly into the next, ensuring reliable data flow and consistent context persistence across coordinated tasks. This enables sequential execution for complex automation scenarios.

What is the best way to build a data processing pipeline for multiple agents?▼

Building a data processing pipeline involves defining custom chains that coordinate sequential steps, streaming data between agents while maintaining memory of prior outputs. This supports deterministic task execution for complex automation scenarios.

Can I use streaming orchestration to automate code review processes across agents?▼

Streaming orchestration supports code review automation by passing analysis outputs from one agent to the next in a deterministic sequence. It maintains memory of prior outputs to ensure consistent context throughout the review pipeline.

Does multi-agent workflow orchestration support custom chains and predefined pipelines?▼

Multi-agent workflow orchestration supports both custom chains for flexible task execution and predefined pipelines for standardized automation. Both methods stream data between agents while ensuring deterministic processing and persistent memory.

Why does coordinated multi-agent task execution require context persistence?▼

Coordinated multi-agent task execution requires context persistence to maintain memory of prior outputs across sequential steps. This ensures reliable data flow, consistent transformations, and accurate orchestration throughout complex automation workflows.

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