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.