rysweet/amplihack-rs/agent-generator-tutor

Claude Code and agent skill by rysweet/amplihack-rs

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Interactive teaching agent for the goal-seeking agent generator and eval system. Provides a structured 14-lesson curriculum covering agent generation, SDK selection, multi-agent architecture, progressive evaluation (L1-L12), retrieval strategies, intent classification, math code generation, self-improvement loops with patch proposer and reviewer voting, and memory export/import.

Install

npx vskill@latest install rysweet/amplihack-rs/agent-generator-tutor

SKILL.md

Agent Generator Tutor Skill

Interactive teaching agent for the goal-seeking agent generator and eval system.

What This Skill Does

Loads the GeneratorTeacher from crates/amplihack-agents/src/teaching/generator_teacher.rs and guides users through a structured 14-lesson curriculum with exercises and quizzes.

Curriculum (14 Lessons)

LessonTitleTopics
L01Introduction to Goal-Seeking AgentsArchitecture, GoalSeekingAgent interface
L02Your First Agent (CLI Basics)Prompt files, CLI invocation, pipeline
L03SDK Selection GuideCopilot, Claude, Microsoft, Mini SDKs
L04Multi-Agent ArchitectureCoordinators, sub-agents, shared memory
L05Agent SpawningDynamic sub-agent creation at runtime
L06Running EvaluationsProgressive test suite, SDK eval loop
L07Understanding Eval Levels L1-L12Core (L1-L6) and advanced (L7-L12) levels
L08Self-Improvement LoopEVAL-ANALYZE-RESEARCH-IMPROVE-RE-EVAL-DECIDE
L09Security Domain AgentsDomain-specific agents and eval
L10Custom Eval LevelsTestLevel, TestArticle, TestQuestion
L11Retrieval ArchitectureSimple, entity, concept, tiered strategies
L12Intent Classification and Math Code GenNine intent types, safe arithmetic
L13Patch Proposer and Reviewer VotingAutomated code patches, 3-perspective review
L14Memory Export/ImportSnapshots, cross-session persistence

How to Use

Start the Tutorial
rust
use amplihack_agents::teaching::GeneratorTeacher;
 
let teacher = GeneratorTeacher::new();
// See what lesson is next
let next_lesson = teacher.get_next_lesson();
println!("Start with: {}", next_lesson.title);
Teach a Lesson
python
content = teacher.teach_lesson("L01")
print(content)  # Full lesson with exercises and quiz questions
Check an Exercise
python
feedback = teacher.check_exercise("L01", "E01-01", "your answer here")
print(feedback)  # PASS or NOT YET with hints
Run a Quiz
python
# 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
python
report = teacher.get_progress_report()
print(report)  # Shows completed/locked/available lessons
Validate Curriculum Integrity
python
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.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

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