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stream-chain

Execute sequential multi-agent workflows by chaining prompt outputs as context.

2|Updated Apr 7, 2026
Search Tags:#automation#workflow#multi-agent#streaming#pipeline#chaining
One-click install
npx skills add https://github.com/acarmonag/ai-runbook-automation --skill stream-chain-acarmonag
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/acarmonag/ai-runbook-automation/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/acarmonag/ai-runbook-automation --skill stream-chain-acarmonag

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the challenge of managing complex, multi-step AI workflows where the output of one task must be processed as the input for the next, ensuring seamless data flow and logical progression.

Core Features & Use Cases

  • Custom Chains: Execute sequences of custom prompts to build bespoke multi-agent workflows.
  • Predefined Pipelines: Utilize battle-tested workflows for common tasks like codebase analysis, refactoring, testing, and optimization.
  • Context Preservation: Automatically maintains state and context across multiple steps, allowing for iterative refinement and complex data transformations.

Quick Start

Use the stream-chain skill to run a custom sequence of prompts for analyzing the codebase structure and generating an improvement plan.

Frequently Asked Questions about stream-chain

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

FAQPage Schema
How do I chain multi-agent workflows to pass output as context for the next step?▼

Multi-agent workflows are executed sequentially by chaining prompt outputs as context for subsequent steps, ensuring seamless data flow and logical progression across complex transformations.

What is the best way to automate codebase refactoring and testing pipelines?▼

Automated refactoring and testing pipelines are facilitated through predefined workflows that execute sequential prompts, automatically preserving context and state across multiple steps for iterative refinement.

Do I need the Claude Flow engine to manage state in streaming pipelines?▼

Yes, integration with the Claude Flow engine is required to manage state, timeouts, and multi-step context propagation within the unified execution environment for streaming pipelines.

How does context preservation work across multiple steps in a multi-agent pipeline?▼

Context preservation automatically maintains state and data across multiple steps in a multi-agent pipeline, allowing for iterative refinement and complex data transformations without manual data passing.

Can I build custom multi-agent workflows for bespoke data transformation tasks?▼

Custom multi-agent workflows can be built by executing sequences of custom prompts, enabling bespoke data transformation and complex processing within a unified execution environment.

Why does my multi-step workflow lose state between automated testing pipelines?▼

State loss in multi-step workflows typically occurs without proper context propagation; this skill requires the Claude Flow engine integration to manage state, timeouts, and context preservation automatically.

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