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stream-chain

Chain agent prompts sequentially to execute multi-step workflows.

Updated Mar 2, 2026
Search Tags:#automation#workflow#multi-agent#orchestration#pipeline#chaining
One-click install
npx skills add https://github.com/ExpertVagabond/ruvector --skill stream-chain-expertvagabond
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/ExpertVagabond/ruvector/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/ExpertVagabond/ruvector --skill stream-chain-expertvagabond

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines complex, multi-step processes by allowing the output of one agent to seamlessly feed into the next, automating sequential tasks and data transformations.

Core Features & Use Cases

  • Custom Chains: Define and execute your own sequences of prompts for bespoke workflows.
  • Predefined Pipelines: Utilize ready-made workflows for common tasks like analysis, refactoring, testing, and optimization.
  • Sequential Data Flow: Ensures context is maintained and passed between each step in the chain.
  • Use Case: Automate the entire process of analyzing a codebase, identifying areas for improvement, and generating an action plan, all within a single command.

Quick Start

Execute a custom chain of prompts to analyze the codebase, identify improvement areas, and generate an action plan.

Frequently Asked Questions about stream-chain

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

FAQPage Schema
How do I automate multi-step agent workflows for sequential data transformations?▼

Multi-step agent workflows are automated by chaining prompts sequentially, allowing the output of one agent to feed directly into the next for sequential data transformations and complex task pipelines.

Can I create custom prompt sequences for code analysis and refactoring pipelines?▼

Custom prompt sequences can be defined and executed to create bespoke workflows for common development tasks including codebase analysis, refactoring, and optimization.

What is the best way to maintain context between chained agents in an automated pipeline?▼

Context is maintained between chained agents by ensuring sequential data flow, passing the context and output from each step directly into the next prompt within the automated pipeline.

Does multi-agent orchestration work with predefined pipelines for code testing?▼

Multi-agent orchestration supports predefined pipelines specifically designed for common development tasks, providing ready-made automated workflows for code testing and analysis.

Why use sequential prompt chaining instead of single-agent prompt execution for complex tasks?▼

Sequential prompt chaining streamlines complex multi-step processes by maintaining context across stages, automating sequential tasks and data transformations that single-agent execution cannot achieve.

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