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.