stream-chain

Chain agent outputs into sequential multi-agent workflows.

1|Updated Mar 20, 2026

Search Tags:#automation #workflow #multi-agent #streaming #orchestration #prompts

Ready to run

npx skills add https://github.com/MSamiulHasnat/ProjectRunningFolder_Programming --skill stream-chain-msamiulhasnat

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/MSamiulHasnat/ProjectRunningFolder_Programming/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/MSamiulHasnat/ProjectRunningFolder_Programming --skill stream-chain-msamiulhasnat

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Stream-chain orchestrates multi-agent workflows by streaming each agent's output into the next step, enabling complex, multi-step data transformations and sequential processing across collaborative AI tasks.

Core Features & Use Cases

Custom Chains: Build bespoke prompt sequences where each step feeds into the next.
Predefined Pipelines: Use battle-tested workflows for common tasks.
Streaming flow: Each step receives the full output from the previous step to enable streaming data flow.
Coordination: Supports coordinated multi-agent workflows and memory-like context propagation.

Quick Start

Create a custom chain that processes input through a sequence of prompts to produce a final result.

Frequently Asked Questions about stream-chain

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

FAQPage Schema

How do I coordinate multi-agent workflows with sequential data processing?▼

Multi-agent workflows coordinate sequential data processing by chaining each agent's output directly into the next step, ensuring streaming data flow and preserving contextual information for cross-step reasoning tasks.

What is the best way to build custom prompt chains for AI automation?▼

Building custom prompt chains for AI automation involves creating bespoke prompt sequences where each step feeds its full output into the next, enabling complex multi-step data transformations and sequential processing.

Can I use predefined pipelines for common software development tasks?▼

Predefined pipelines provide battle-tested workflows for common software development tasks, allowing you to apply structured multi-agent coordination without building custom chains from scratch.

Does multi-agent orchestration preserve context across sequential steps?▼

Multi-agent orchestration preserves context across sequential steps by propagating memory-like contextual information, ensuring each step receives the full output from the previous step for accurate cross-step reasoning.

When should I use predefined pipelines instead of custom chains?▼

Use predefined pipelines for common, battle-tested workflows to ensure reliability, while custom chains are better suited for building bespoke sequences that require highly specific data transformations or unique automation logic.