personalize-chapter

name	personalize-chapter
description	Generates a personalized version of any curriculum chapter adapted to the learner's skill level, professional background, and language preference. Uses the learner's profile from Neon Postgres combined with Qdrant-retrieved chapter content.
license	MIT
allowed-tools	neon_postgres_read qdrant_search gemini_generate redis_cache
metadata	{"author":"Ayesha Khalid","version":"1.0.0","category":"education","domain":"personalization","requires_auth":"true","cache_ttl":"3600"}

Personalize Chapter Skill

Purpose

Rewrite any curriculum chapter to match the learner's background so it feels written specifically for them. A software engineer gets code-heavy analogies; a hardware engineer gets circuit-level comparisons; a student gets first-principles explanations.

Prerequisites

• Learner must be authenticated (valid RS256 JWT)
• Learner profile must exist in Neon Postgres (users table)

Instructions

1. Validate JWT via the JWKS endpoint (/.well-known/jwks.json)
2. Fetch learner profile from Neon Postgres:
   • skill_level: beginner | intermediate | advanced
   • background: software_engineer | hardware_engineer | roboticist | student
   • preferred_language: en | ur | (other)
3. Retrieve chapter chunks from Qdrant (filter by chapter_id metadata field, top-k=20)
4. Check Redis cache — key: personalize:{user_id}:{chapter_id}:{profile_hash} (TTL: 1 hour). Return cached version if hit.
5. Build the personalization prompt:

   You are Aria, personalizing a robotics curriculum chapter.
   The learner is a {background} with {skill_level} level.
   Language: {language}.
   
   Rewrite the following chapter content so it:
   - Uses analogies from {background} domain
   - Adjusts technical depth for {skill_level}
   - Keeps all code blocks unchanged
   - If language is Urdu: translate prose to Urdu RTL, keep code in English
   
   [CHAPTER CONTENT]
   {chapter_chunks}
   

6. Stream the personalized chapter via SSE
7. Cache the result in Redis (TTL: 1 hour)

Supported Personalizations

Background	Adaptation Strategy
software_engineer	Emphasize API contracts, async patterns, type systems
hardware_engineer	Connect to electrical signals, timing, I/O interfaces
roboticist	Use kinematics/dynamics framing, reference URDF/SDF directly
student	First-principles explanations, more worked examples, slower pacing

Constraints

• Code blocks (\```) are NEVER modified or translated
• ROS 2 package names, topic names, and CLI commands stay in English always
• Personalized output must include skill_level and background in the response metadata

Error Handling

Scenario	Response
Unauthenticated request	401 + login modal redirect
Profile not found	Use default: beginner + student + English
Chapter not in Qdrant	"This chapter has not been indexed yet. Try the standard version."
Gemini failure	Fall back to OpenAI; if both fail, return the original chapter with a notice