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.