How do I install the Agent Generator Tutor skill?

Run npx skillmds add rysweet/agent-generator-tutor in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Agent Generator Tutor safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Agent Generator Tutor?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Agent Generator Tutor free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Agent Generator Tutor?

rysweet (@rysweet) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Agent Generator Tutor by rysweet — Agent Skill

Name: Agent Generator Tutor
Rating: 4.2 (5 reviews)
Author: rysweet

Agent Generator Tutor

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.

rysweet 0b62f6b 3.9 KB Updated Sep 9, 2026

File contents

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)

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

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

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.

---name: agent-generator-tutordescription: 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.--- # 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) | 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 ```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.

Frequently asked questions