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stream-chain

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

Updated Mar 5, 2026
Search Tags:#automation#workflow#multi-agent#streaming#orchestration#pipeline#data-transformation
One-click install
npx skills add https://github.com/fabri07/Vektor --skill stream-chain-fabri07
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stream-chain
Source: https://github.com/fabri07/Vektor/tree/main/.claude/skills/stream-chain
Command: npx skills add https://github.com/fabri07/Vektor --skill stream-chain-fabri07

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Stream complex, multi-agent tasks by streaming each step's output into the next, enabling seamless sequential processing and orchestration.

Core Features & Use Cases

  • Custom Chains (run): define a sequence of prompts where each step uses the previous output to build on results.
  • Predefined Pipelines (pipeline): reuse battle-tested workflows for common tasks like analysis, refactor, testing, and optimization.
  • Use Case: orchestrate code analysis, data transformations, and automation across teams with continuous context.

Quick Start

Create a 3-step custom chain to analyze a task and produce a concrete 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 orchestrate multi-step AI workflows with continuous context?▼

Orchestrate multi-step AI workflows by streaming each step's output directly into the next, ensuring continuous sequential processing. This approach enables seamless context transfer across custom chains and predefined pipelines for complex task automation.

Can I use predefined pipelines for code analysis and refactoring?▼

Yes, you can reuse predefined pipelines for common development tasks like code analysis, refactoring, testing, and optimization. These battle-tested workflows streamline automation across teams without requiring manual step configuration.

What's the best way to build a custom sequence of prompts for data transformation?▼

Building a custom chain using the run mode lets you define a sequence of prompts where each step uses the previous output. This is ideal for iterative data transformations and producing concrete action plans.

Does multi-agent workflow orchestration support configurable timeouts and logging?▼

Multi-agent workflow orchestration supports configurable timeouts, verbose logging, and memory integration. These features ensure reliable execution and debugging visibility across custom chains and predefined pipelines.

Do I need to define clear steps before running a multi-agent pipeline?▼

Yes, multi-agent pipelines require clear step definitions to function correctly. Defining each step ensures the orchestration engine properly streams prior outputs into the next stage for reliable task processing.

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