Agent Generator Tutor Skill Interactive teaching agent for the goal-seeking agent generator and eval system. What This Skill Does Loads the GeneratorTeacher from src/amplihack/agents/teaching/generator_teacher.py and guides users through a structured 14-lesson curriculum with exercises and quizzes. Curriculum (14 Lessons) Lesson Title Topics L01 Introduction to Goal-Seeking Agents Architecture, GoalSeekingAgent interface L02 Your First Agent (CLI Basics) Prompt files, CLI invocation, pipeline L03 SDK Selection Guide Copilot, Claude, Microsoft, Mini SDKs L04 Multi-Agent Architecture Coordinators, sub-agents, shared memory L05 Agent Spawning Dynamic sub-agent creation at runtime L06 Running Evaluations Progressive test suite, SDK eval loop L07 Understanding Eval Levels L1-L12 Core (L1-L6) and advanced (L7-L12) levels L08 Self-Improvement Loop EVAL-ANALYZE-RESEARCH-IMPROVE-RE-EVAL-DECIDE L09 Security Domain Agents Domain-specific agents and eval L10 Custom Eval Levels TestLevel, TestArticle, TestQuestion L11 Retrieval Architecture Simple, entity, concept, tiered strategies L12 Intent Classification and Math Code Gen Nine intent types, safe arithmetic L13 Patch Proposer and Reviewer Voting Automated code patches, 3-perspective review L14 Memory Export/Import Snapshots, cross-session persistence How to Use Start the Tutorial from amplihack . agents . teaching . generator_teacher import GeneratorTeacher teacher = GeneratorTeacher ( )

See what lesson is next

next_lesson

teacher . get_next_lesson ( ) print ( f"Start with: { next_lesson . title } " ) Teach a Lesson content = teacher . teach_lesson ( "L01" ) print ( content )

Full lesson with exercises and quiz questions

Check an Exercise feedback = teacher . check_exercise ( "L01" , "E01-01" , "your answer here" ) print ( feedback )

PASS or NOT YET with hints

Run a Quiz

Self-grading mode (see correct answers)

result

teacher . run_quiz ( "L01" )

Provide answers for grading

result

teacher . run_quiz ( "L01" , answers = [ "PromptAnalyzer" , "Explains stored knowledge" , "False" ] ) print ( f"Score: { result . quiz_score : .0% } , Passed: { result . passed } " ) Check Progress report = teacher . get_progress_report ( ) print ( report )

Shows completed/locked/available lessons

Validate Curriculum Integrity validation = teacher . validate_tutorial ( ) print ( f"Valid: { validation [ 'valid' ] } , Issues: { validation [ 'issues' ] } " ) Prerequisites Each lesson has prerequisites that must be completed first. The curriculum follows a dependency graph ensuring foundational concepts are learned before advanced topics. Exercise Validators The teaching agent includes 15 specialized validators that check user answers for correctness. Exercises without explicit validators use a fallback that checks for key phrases from the expected output.