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rag-new

Automate RAG pipeline design and evaluation with RAGAS.

Updated Jun 21, 2026
Search Tags:#data science#RAG#pipeline automation#RAGAS#ML lifecycle
One-click install
npx skills add https://github.com/infantesromeroadrian/arca-agent --skill rag-new
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag-new
Source: https://github.com/infantesromeroadrian/arca-agent/tree/main/template/skills/rag-new
Command: npx skills add https://github.com/infantesromeroadrian/arca-agent --skill rag-new

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the design and evaluation of a complete RAG pipeline from concept to implementation, streamlining the process for data scientists and ML engineers.

Core Features & Use Cases

  • Pipeline Automation: Automates the creation of a RAG pipeline from initial design to final evaluation.
  • RAGAS Integration: Utilizes RAGAS for comprehensive evaluation of the RAG pipeline.
  • Use Case: Ideal for kickstarting a new RAG project, where you need to establish a robust pipeline with chunking, embeddings, retrieval, reranker, and generator functionalities.

Quick Start

Invoke the 'rag-new' command to initiate the RAG pipeline design process.

Frequently Asked Questions about rag-new

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

FAQPage Schema
How do I automate the design and evaluation of a RAG pipeline?▼

Automating a RAG pipeline involves coordinating data validation, architecture design, and model evaluation. This Skill streamlines that process from initial design to final assessment using RAGAS, covering chunking, embeddings, retrieval, reranker, and generator functionalities.

What is the best way to evaluate my RAG pipeline using RAGAS?▼

Evaluating a RAG pipeline with RAGAS requires comprehensive assessment of the retrieval and generation components. This Skill integrates RAGAS directly into the ML lifecycle to provide final evaluation metrics for your automated architecture design.

How do I start a new Retrieval-Augmented Generation project from scratch?▼

Starting a new RAG project requires establishing a robust pipeline with chunking, embeddings, retrieval, reranker, and generator functionalities. This Skill kickstarts the process by automating pipeline creation from initial design considerations to implementation.

Does this RAG pipeline automation support the entire ML lifecycle?▼

RAG pipeline automation supports the entire ML lifecycle by coordinating multiple steps from data validation to final model evaluation. It streamlines the process for data scientists and ML engineers working on Retrieval-Augmented Generation projects.

What components do I need for a complete RAG architecture?▼

A complete RAG architecture requires chunking, embeddings, retrieval, reranker, and generator functionalities. This Skill automates the design of these components and coordinates data validation to ensure a robust pipeline setup.

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