stream-chain

Chain outputs across sequential steps to coordinate multi-agent workflows.

3|Updated Mar 7, 2026

Search Tags:#automation #workflow #agent-coordination #mult-agent #ai-pipeline #stream-chain

Ready to run

npx skills add https://github.com/nidhi-subrah/HackCanada2026 --skill stream-chain-nidhi-subrah

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/nidhi-subrah/HackCanada2026/tree/main/.agents/skills/stream-chain
Command: npx skills add https://github.com/nidhi-subrah/HackCanada2026 --skill stream-chain-nidhi-subrah

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Stream-chain addresses the complexity of coordinating multi-agent workflows by passing outputs from one step to the next, enabling cohesive and auditable pipelines.

Core Features & Use Cases

Orchestrates two modes: Custom Chains (run) and Predefined Pipelines (pipeline) to suit flexible or standard tasks.
Supports memory integration and swarm coordination for persistence and multi-agent collaboration.
Useful for code analysis, deployment automation, data processing, and QA workflows in software engineering.

Quick Start

To start a custom chain, invoke the flow with a sequence of prompts.

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 by chaining outputs sequentially?▼

Multi-agent workflow coordination chains outputs from one step to the next, creating cohesive and auditable pipelines. You can orchestrate custom chains or predefined pipelines to pass iterative prompts and memory across sequential steps.

What is the best way to automate code analysis and deployment pipelines with multiple agents?▼

Automating code analysis and deployment pipelines uses predefined pipelines to execute standard software engineering tasks. This approach applies multi-agent coordination and memory integration to maintain persistence across iterative workflow steps.

Can I use custom chain execution for data processing tasks that require memory of previous results?▼

Custom chain execution supports data processing tasks by maintaining memory of previous results across sequential prompts. You can invoke the flow with a sequence of prompts to pass iterative outputs through the chain.

Does multi-agent workflow coordination support configurable timeouts and error handling?▼

Multi-agent workflow coordination includes configurable timeouts, verbose logging, and error handling for custom chain execution. These features ensure pipeline stability during iterative prompts and multi-agent collaboration.

What is the difference between running custom chains and predefined pipelines for AI workflows?▼

Custom chains offer flexible execution by running a sequence of prompts, while predefined pipelines suit standard tasks. Both modes support memory integration and swarm coordination for multi-agent collaboration in software development.

When should I not use sequential chain execution for software engineering tasks?▼

Sequential chain execution is not ideal for software engineering tasks requiring parallel processing or independent agent actions without memory dependencies. It is designed for iterative prompts where outputs must pass sequentially across steps.