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multi-model-orchestrator

Orchestrate multi-stage AI pipelines with model routing and parallel execution.

3|Updated Feb 3, 2026
Search Tags:#llm#rag#multi-agent#orchestration#model routing#ai pipeline
One-click install
npx skills add https://github.com/paolomoz/skills --skill multi-model-orchestrator-paolomoz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-model-orchestrator
Source: https://github.com/paolomoz/skills/tree/main/skills/multi-model-orchestrator
Command: npx skills add https://github.com/paolomoz/skills --skill multi-model-orchestrator-paolomoz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill tackles the complexity of building multi-stage AI pipelines by intelligently routing tasks to the most suitable AI models, optimizing for cost, speed, and capability.

Core Features & Use Cases

  • Multi-Stage Pipelines: Design complex workflows with distinct stages for classification, reasoning, content generation, and image creation.
  • Model Routing: Dynamically select the best model (e.g., fast for classification, powerful for reasoning) based on the task's role and latency requirements.
  • Parallel Processing: Execute independent tasks (like generating multiple content blocks or images) concurrently for significant speedups.
  • RAG Integration: Seamlessly incorporate Retrieval Augmented Generation by fetching context from vector databases.
  • Multi-Agent Analysis: Run prompts across different AI models for comparative insights and synthesis.
  • Use Case: Automate the creation of a blog post by first classifying the user's intent, then reasoning about the content structure, generating multiple sections in parallel, and finally creating accompanying images.

Quick Start

Use the multi-model-orchestrator skill to build a pipeline that classifies user intent, reasons about content, generates text blocks in parallel, and creates images.

Frequently Asked Questions about multi-model-orchestrator

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

FAQPage Schema
How do I route different tasks in an AI pipeline to the most suitable LLM?▼

Model routing in an AI pipeline assigns tasks to optimal models based on role, using fast models for classification and powerful ones for reasoning. This optimizes cost, speed, and capability across multi-stage workflows.

How do I build a multi-stage AI pipeline that generates text and images in parallel?▼

You can build a multi-stage AI pipeline by defining distinct stages for classification, reasoning, content generation, and image creation. Independent stages like generating multiple content blocks execute concurrently for significant speedups.

Can I use Retrieval Augmented Generation with multi-agent orchestration?▼

Retrieval Augmented Generation integrates with multi-agent orchestration by fetching context from vector databases. This allows multiple agents across different LLMs to analyze and synthesize comparative insights using retrieved data.

What is the best way to handle LLM timeouts and failures during model routing?▼

Handling LLM failures during model routing requires timeout protection and fallback mechanisms. These ensure robust execution by catching stalled stages and switching to alternative models to maintain pipeline continuity.

Does multi-agent orchestration support real-time progress streaming for AI pipelines?▼

Multi-agent orchestration supports real-time progress streaming via SSE. This provides immediate feedback on pipeline execution status while tasks are routed to different models for processing.

When should I use multi-model orchestration instead of a single LLM for content generation?▼

Use multi-model orchestration instead of a single LLM when workflows require distinct stages like intent classification, structural reasoning, parallel text generation, and image creation, optimizing overall cost and speed.

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