stream-chain

Chain outputs between steps to orchestrate multi-agent workflows with streaming data flow.

Updated Aug 23, 2026

Search Tags:#automation #workflow #streaming #orchestration #pipeline #data-integration #mult-agent

Ready to run

npx skills add https://github.com/JacobJ215/sharpedge --skill stream-chain-jacobj215

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/JacobJ215/sharpedge/tree/main/.agents/skills/stream-chain
Command: npx skills add https://github.com/JacobJ215/sharpedge --skill stream-chain-jacobj215

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Stream-Chain enables orchestrating multi-agent workflows by chaining outputs between agents, allowing complex data transformations and sequential processing.

Core Features & Use Cases

Custom Chains (run): Execute custom prompt sequences with full control
Predefined Pipelines (pipeline): Use battle-tested workflows for common tasks
Each step receives the complete output from the previous step, enabling sophisticated multi-agent coordination through streaming data flow.

Quick Start

Create a simple three-step chain that analyzes input, transforms results, and outputs 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 by chaining outputs between steps?▼

Multi-agent workflow orchestration passes the complete output from one processing step directly to the next, enabling sequential data transformation. This streaming data flow supports custom chains and predefined pipelines for progressive task refinement.

Can I configure per-step timeouts and prompts for sequential data pipelines?▼

Sequential data pipelines support configurable prompts and per-step timeout options. You can control custom prompt sequences and apply verbose or debug settings to monitor the workflow execution at each stage.

What is the difference between custom chains and predefined pipelines for multi-agent coordination?▼

Custom chains allow you to execute your own prompt sequences for full control, while predefined pipelines offer battle-tested workflows for common tasks. Both methods chain outputs between agents to enable streaming data flow.

Does context get stored between steps when transforming streaming data?▼

Context is stored between steps during streaming data transformation. This allows the workflow to maintain state across sequential coordination, ensuring each agent receives the complete output from the previous step for sophisticated multi-agent processing.

When should I use streaming data flow for progressive refinement across tasks?▼

Use streaming data flow when tasks require sequential coordination and progressive refinement. It is ideal for complex data transformations where each step must process the complete output of the previous agent before proceeding.