stream-chain

Stream outputs between steps to orchestrate multi-agent workflows.

1|Updated Dec 2, 2025

Search Tags:#automation #workflow #multi-agent #streaming #orchestration #pipeline #coordination

Ready to run

npx skills add https://github.com/danilonovaisv/PORTFOLIO-DANILO-FINAL --skill stream-chain-danilonovaisv

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Stream-Chain enables coordinating complex, multi-step workflows by chaining the output of one agent to the next, reducing manual handoffs and speeding up multi-agent collaboration.

Core Features & Use Cases

Custom Chains: define bespoke sequential prompts where each step feeds into the next.
Predefined Pipelines: reuse battle-tested workflows for common tasks (analysis, refactor, test, optimize).
Streaming Context: maintain end-to-end context across steps for coherent results and auditability.
Use Cases: building AI-assisted data processing pipelines, code analysis with stepwise refinement, and end-to-end feature development workflows.

Quick Start

Create a basic stream chain that analyzes a codebase and generates an improvement plan.

Frequently Asked Questions about stream-chain

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

FAQPage Schema

How do I build a multi-agent workflow for stepwise code analysis?▼

To build a multi-agent workflow for stepwise code analysis, you orchestrate custom agent chains that stream outputs sequentially, passing refined context from one step to the next for progressive transformation.

What is streaming context in a multi-agent pipeline?▼

Streaming context in a multi-agent pipeline is the mechanism of passing the output of one agent directly as the input to the next, maintaining end-to-end context across steps for coherent and auditable results.

What's the best way to coordinate complex data transformations across multiple agents?▼

The best way to coordinate complex data transformations across multiple agents is using a predefined pipeline that orchestrates execution flow with optional timeout and debug controls, reducing manual handoffs.

Do I need predefined pipelines to automate multi-agent collaboration?▼

You do not need predefined pipelines to automate multi-agent collaboration, as you can define custom chains with bespoke sequential prompts, but predefined pipelines offer reusable workflows for common tasks like refactoring.

Can I define custom chains with bespoke sequential prompts for multi-agent orchestration?▼

You can define custom chains with bespoke sequential prompts for multi-agent orchestration, requiring a sequence of at least two steps with clear input prompts for each stage to manage execution flow.

What are the limitations of streaming outputs between agent steps?▼

A limitation of streaming outputs between agent steps is the requirement of a minimum of two steps, and without optional timeout controls, long-running execution flows in complex pipelines may hang or delay final outputs.