stream-chain

Stream outputs between steps to orchestrate multi-agent workflows.

Updated Apr 8, 2026
One-click install
npx skills add https://github.com/SlevoDev/s-tag --skill stream-chain-slevodev
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/SlevoDev/s-tag/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/SlevoDev/s-tag --skill stream-chain-slevodev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates multi-agent workflows by streaming each step's output into the next, reducing manual handoffs and improving end-to-end traceability.

Core Features & Use Cases

  • Stream-based chaining: connect multiple agents and prompts to form a cohesive pipeline.
  • Predefined pipelines and custom chains: choose between builder-style prompts or battle-tested sequences for common tasks.
  • Cross-step context: propagate the entire previous output to each subsequent step, enabling richer collaboration and automation.

Quick Start

Provide prompts to Claude Flow to execute sequential steps.

Frequently Asked Questions about stream-chain

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

FAQPage Schema
What is stream-based chaining for multi-agent workflows?

Stream-based chaining orchestrates multi-agent workflows by streaming each step's output directly into the next, reducing manual handoffs and improving end-to-end traceability across complex data transformations.

How do I automate complex data transformation pipelines across multiple agents?

You can automate complex data transformation pipelines by providing sequential prompts to configure modular steps, utilizing memory-backed context to propagate entire previous outputs to subsequent agents for richer collaboration.

Can I use predefined pipelines for common software engineering tasks?

Yes, you can choose between builder-style custom prompts to connect multiple agents or battle-tested predefined sequences designed for common software engineering tasks to form a cohesive pipeline.

Does multi-agent orchestration support configurable timeouts for robust deployments?

Multi-agent orchestration supports configurable timeouts and memory-backed context, ensuring robust deployments when applying sequential processing across software engineering tasks and complex data pipelines.

What is the best way to propagate context across sequential processing steps?

The best way to propagate context is using cross-step streaming, which passes the entire previous output to each subsequent step, enabling richer collaboration and automation throughout the workflow.

Why use stream-chain instead of manual handoffs for multi-agent workflows?

Using stream-chain eliminates manual handoffs by automatically streaming outputs between agents, which significantly improves end-to-end traceability and ensures cohesive pipeline execution for complex transformations.