Skill: User Profiling

Skill Name: user-profiling

Purpose: Collect and manage user profile information during signup to enable content personalization throughout their learning journey.

Description

This skill is responsible for gathering comprehensive user background information through a structured 5-question survey. The collected data is used to:

• •Create personalized learning paths
• •Recommend appropriate modules and content
• •Adjust content complexity based on experience level
• •Provide hardware-specific learning resources
• •Filter code examples by language preference

Input Schema

{
  operation: "collect" | "retrieve" | "update" | "analyze",
  user_id?: string,           // Required for retrieve/update/analyze
  responses?: ProfileResponse[] // Required for collect/update
}

Profile Questions

1. •

   Programming Experience (experience_level)

   • •beginner: Beginner (< 1 year coding)
   • •intermediate: Intermediate (1-3 years)
   • •advanced: Advanced (3+ years)
2. •

   ROS Familiarity (ros_familiarity)

   • •none: No experience
   • •basic: Basic (completed tutorials)
   • •proficient: Proficient (built ROS projects)
3. •

   Hardware Access (hardware_access)

   • •simulation: Simulation only (no physical hardware)
   • •jetson: Jetson kit with sensors
   • •full_lab: Full robot lab (robot + sensors + workstation)
4. •

   Learning Goal (learning_goal)

   • •career: Career advancement in robotics
   • •research: Academic research
   • •hobby: Personal interest / hobby
5. •

   Preferred Code Language (preferred_language)

   • •python: Python
   • •cpp: C++
   • •both: Both (show all examples)

Output Schema

Successful Response

{
  success: true,
  data: {
    profile: UserProfile,        // Complete profile object
    analysis: ProfileAnalysis,   // Personalization recommendations
    completeness?: number,       // 0-100% (for collect)
    user_id?: string,           // Generated ID (for collect)
    updated?: boolean           // True for update operations
  }
}

Profile Analysis

{
  persona: string,              // "Beginner Hobbyist", "Advanced Researcher", etc.
  content_level: "simplified" | "standard" | "advanced",
  recommended_modules: string[], // Priority-ordered module list
  estimated_completion_time: string, // e.g., "16 weeks"
  personalization_hints: {
    show_prerequisites: boolean,
    include_research_papers: boolean,
    hardware_specific_content: string,
    code_language_filter: string
  }
}

Error Response

{
  success: false,
  errors: string[],            // Validation or system errors
  warnings?: string[]          // Incomplete profile warnings
}

Operations

1. Collect (operation: "collect")

• •Purpose: Create a new user profile
• •Requirements: All 5 questions must be answered
• •Output: Created profile with analysis

2. Retrieve (operation: "retrieve")

• •Purpose: Get existing user profile
• •Requirements: user_id must be provided
• •Output: Complete profile data

3. Update (operation: "update")

• •Purpose: Modify existing profile
• •Requirements: user_id and at least one response
• •Output: Updated profile with new analysis

4. Analyze (operation: "analyze")

• •Purpose: Generate recommendations for existing profile
• •Requirements: user_id must be provided
• •Output: Profile analysis without full profile data

Validation Rules

• •All 5 questions are required for profile creation
• •Response values must match predefined options
• •Profile hash generated from key fields for caching
• •Completeness score calculated for partial profiles

Storage

• •Database: Neon PostgreSQL
• •Table: user_profiles
• •Indexes: profile_hash, experience_level, ros_familiarity, hardware_access
• •Fields: user_id, 5 profile fields, profile_hash, timestamps

Privacy & Security

• •Profile data used exclusively for personalization
• •Users can view and delete their profiles
• •No data shared with third parties
• •Anonymized analytics only
• •Profile hashes enable content caching without exposing PII

Use Cases

1. •New User Registration: Collect initial profile during signup
2. •Progress Checkpoint: Update profile as skills advance
3. •Content Recommendation: Analyze profile for module suggestions
4. •A/B Testing: Group users by similar profiles
5. •Learning Path Optimization: Adjust paths based on hardware access

Integration Points

• •Learning Management System: Provides profile data for content filtering
• •Content Delivery: Uses analysis for module recommendations
• •Analytics Platform: Consumes anonymized profile insights
• •User Dashboard: Displays profile and learning progress