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Hackathon-4-Course-Companion-FTE-
Hackathon-4-Course-Companion-FTE- enthält 13 gesammelte Skills von NaveedTechLab, mit Repository-Berufsabdeckung und Skill-Detailseiten auf SkillsMP.
Skills in diesem Repository
Expert in developing deterministic FastAPI backends for content delivery, navigation, and rule-based quiz grading. Knowledgeable in Zero-Backend-LLM principles, ensuring no LLM calls are made in the core API routes.
Skilled in developing Next.js/React web applications for Phase 3 and integrating with the OpenAI Apps SDK for Phase 1. Focuses on responsive LMS dashboards and conversational UI integration.
Expert in selective LLM integration using Claude Agent SDK. Specialized in implementing premium-gated features like adaptive learning paths and LLM-graded assessments while maintaining strict isolation from deterministic logic.
Specialist in configuring Cloudflare R2 for verbatim content storage and media assets. Experienced in deploying Python applications to Fly.io or Railway and managing Neon/Supabase database integrations for progress tracking.
Specialized in code review and API auditing to ensure Zero-Backend-LLM compliance in Phase 1. Responsible for verifying that no forbidden LLM API calls, RAG summarization, or agent loops exist in the backend.
Explains educational concepts in a clear, step-by-step manner with examples and analogies
Tracks and motivates learning progress with streaks, achievements, and personalized encouragement
Creates, administers, and grades educational quizzes with immediate feedback
Uses Socratic questioning method to guide users to discover answers themselves
Explains educational concepts in a clear, step-by-step manner with examples and analogies. Uses deterministic algorithms to generate explanations based on available content.
Tracks and motivates learning progress with streaks, achievements, and personalized encouragement using deterministic algorithms to generate motivational content based on user progress data.
Creates, administers, and grades educational quizzes with immediate feedback using rule-based evaluation systems. No LLM calls for grading.
Uses Socratic questioning method to guide users to discover answers themselves through deterministic question generation based on educational content and learning objectives.