stream-chain

Stream each step's output into the next for multi-agent workflows.

Updated Apr 12, 2026
One-click install
npx skills add https://github.com/softmg/product-tracker --skill stream-chain-softmg
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/softmg/product-tracker/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/softmg/product-tracker --skill stream-chain-softmg

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Stream-chain enables reliable orchestration of multi-agent workflows by streaming each step's output into the next, reducing manual handoffs and enabling complex, data-driven pipelines.

Core Features & Use Cases

  • Custom Chains: Build flexible, sequential prompts where each step consumes the previous output.
  • Predefined Pipelines: Use battle-tested workflows to speed up common tasks like analysis, refactor, test, and optimize.
  • Memory & Context Propagation: Persist context across steps and sessions for continuity.
  • Use Case: Coordinate data transformation and analysis across multiple agents in product discovery or code analysis tasks.

Quick Start

Run a basic two-step chain to demonstrate streaming outputs and sequential processing.

Frequently Asked Questions about stream-chain

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

FAQPage Schema
How do I orchestrate a multi-agent workflow with streaming data?

Multi-agent workflow orchestration with streaming data coordinates sequential steps by streaming each agent's output directly into the next. This enables cohesive data processing pipelines for complex tasks without manual handoffs between agents.

What is the best way to build a sequential prompt pipeline for code analysis?

Building a sequential prompt pipeline for code analysis uses custom chains where each step consumes the previous output. Predefined pipelines also provide battle-tested workflows to speed up common tasks like analysis, refactor, test, and optimize.

Can I persist context across multiple agents in a data transformation pipeline?

You can persist context across multiple agents in a data transformation pipeline through memory and context propagation. This feature ensures continuity across sequential steps and sessions, maintaining shared state throughout the workflow.

Does multi-agent orchestration support deterministic step execution and configurable timeouts?

Multi-agent orchestration supports deterministic step execution and configurable timeouts to ensure reliable processing. It applies deterministic rules to sequential prompts, guaranteeing consistent agent execution with optional custom pipelines for specific scenarios.

When do I need streaming output orchestration for my automation pipeline?

You need streaming output orchestration for your automation pipeline when coordinating data transformation and analysis across multiple agents. It is essential for scenarios requiring cohesive data processing, such as product discovery or code analysis tasks.