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ayeshakhalid192007-dev
ayeshakhalid192007-devCOMMUNITY
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personalize-chapter

Generate personalized curriculum chapters from Qdrant chunks and Neon Postgres profiles.

1|Updated Feb 19, 2026
Search Tags:#personalization#gemini#curriculum#qdrant#redis-cache#jwt-auth#neon-postgres
One-click install
npx skills add https://github.com/ayeshakhalid192007-dev/humanoid-ai-studio --skill personalize-chapter
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: personalize-chapter
Source: https://github.com/ayeshakhalid192007-dev/humanoid-ai-studio/tree/main/skills/personalize-chapter
Command: npx skills add https://github.com/ayeshakhalid192007-dev/humanoid-ai-studio --skill personalize-chapter

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Learners receive one-size-fits-all curriculum chapters that do not match their background, skill level, or language, causing confusion and slowed progress; this Skill generates chapter content tailored to an individual's profile to improve comprehension and engagement.

Core Features & Use Cases

  • Fetches authenticated learner profiles from Neon Postgres and retrieves chapter chunks from Qdrant to compose context-aware input.
  • Builds a domain-aware prompt that adapts analogies, technical depth, and language while preserving code blocks, ROS names, and CLI commands.
  • Streams personalized output via SSE and caches results in Redis for fast subsequent access; includes fallbacks to alternate LLMs when streaming fails.
  • Use Case: Convert a robotics chapter into Urdu for a beginner hardware engineer while keeping all code samples and ROS identifiers intact.

Quick Start

Personalize chapter 3 for user 123 with background hardware_engineer, skill_level advanced, and preferred_language ur.

Frequently Asked Questions about personalize-chapter

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

FAQPage Schema
How do I personalize curriculum chapters for different learner backgrounds?▼

The Skill personalizes curriculum chapters by fetching learner profiles from Neon Postgres and retrieving robotics chapter chunks from Qdrant. It then streams tailored content via SSE while preserving code blocks and ROS identifiers.

Can I translate robotics course content to another language while keeping code samples intact?▼

Yes, the Skill translates robotics curriculum chapters into preferred languages like Urdu while strictly preserving code blocks, ROS identifiers, and CLI commands. The domain-aware prompt ensures technical accuracy remains intact during translation.

How does SSE streaming work with Gemini and OpenAI fallback for content generation?▼

SSE streaming pushes personalized curriculum content to clients progressively via Gemini, with automatic fallback to OpenAI if streaming fails. This ensures continuous content delivery even when the primary LLM provider is unavailable.

Do I need RS256 JWT validation to retrieve personalized learning profiles?▼

Yes, RS256 JWT validation via JWKS is required to authenticate requests before retrieving learner profiles from Neon Postgres. This security measure ensures only authorized users access personalized curriculum content.

What are the limitations of using Redis caching for personalized educational content?▼

Redis caching with TTL stores generated curriculum content for fast subsequent access, but cached results may not reflect immediate learner profile updates. Users changing skill level or language preference must wait for cache expiration to see new content.

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