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rag-personalizer

Personalize RAG-retrieved textbook content using a 10-dimension learner profile.

Updated Dec 29, 2025
Search Tags:#education#personalization#rag#learning#openai#adaptive#profile
One-click install
npx skills add https://github.com/FAIQahm/hackathon_I_book --skill rag-personalizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rag-personalizer
Source: https://github.com/FAIQahm/hackathon_I_book/tree/main/.claude/skills/rag-personalizer
Command: npx skills add https://github.com/FAIQahm/hackathon_I_book --skill rag-personalizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai, pydantic, and includes scripts (resource) and assets (resource) components.

What problem does it solve?

Personalizes textbook content by aligning it with a 10-dimension learner profile to deliver customized learning experiences and improve engagement.

Core Features & Use Cases

  • RAG-based content adaptation: personalizes material from retrieval outputs to match learner dimensions (style, level, pace, language, and depth).
  • Profile-driven personalization: creates, updates, and uses learner profiles to guide content transformation.
  • RAG integration readiness: supports optional Qdrant-based retrieval pipelines and multi-language support (en/ur).

Quick Start

Personalize a given textbook excerpt by providing a profile ID and the content to the Personalizer, then retrieve the adapted content.

Frequently Asked Questions about rag-personalizer

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

FAQPage Schema
How do I personalize textbook content for different learner profiles?▼

To personalize textbook content, this Skill applies a 10-dimension learner profile to adapt retrieved RAG material, transforming language, depth, pace, and knowledge level to match individual learning needs.

Does RAG content adaptation support multi-language learning profiles?▼

Yes, RAG content adaptation supports multi-language learning profiles, specifically offering English and Urdu language outputs to tailor educational material for diverse linguistic backgrounds.

Can I use Qdrant for retrieval pipelines with OpenAI content personalization?▼

Yes, you can use Qdrant for retrieval pipelines, as the OpenAI-driven content personalization supports optional Qdrant integration to fetch and transform educational material based on learner profiles.

How do I manage learner profiles for adaptive educational content generation?▼

You manage learner profiles for adaptive content generation using built-in CRUD operations, allowing you to create, update, and retrieve 10-dimension profiles that guide how educational text is transformed.

What are the limitations of using profile-driven RAG for educational content?▼

A limitation of profile-driven RAG for educational content is its dependency on OpenAI and Pydantic, requiring external API access and structured data validation to execute profile-based text transformations.

What is the best way to adapt retrieval outputs to match a learner's pace and depth?▼

The best way to adapt retrieval outputs to match pace and depth is applying a 10-dimension learner profile through the prompts-driven OpenAI pipeline, which transforms RAG-retrieved material across multiple educational dimensions.

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