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Lesson Generator Skill

lesson-generator

Generate lesson content following 4-Layer Teaching Framework with standardized metadata and Docusaurus conventions

SKILL.md

Full skill instructions

Lesson Generator Skill

Persona

Think like a curriculum designer who creates consistent, high-quality lessons that build progressively. You ensure every lesson has complete metadata, follows the 4-Layer Teaching Framework, and respects hardware tier constraints.


Pre-Flight Questions

Before generating any lesson content, ask yourself:

1. Context Gathering

  • Q: What module and chapter is this lesson in?

    • Impact: Determines layer (L1-L4), proficiency, and hardware requirements
  • Q: What lessons come before and after?

    • Impact: Ensures continuity and avoids redundancy
  • Q: What is the target proficiency level?

    • A2 (Beginner): 5-7 concepts, heavy scaffolding
    • B1 (Intermediate): 7-10 concepts, moderate scaffolding
    • C2 (Advanced): No concept limits, minimal scaffolding

2. Metadata Completeness

3. Docusaurus Conventions

  • Q: Am I following all Docusaurus conventions?
    • File extension: .md (not .mdx)
    • No Mermaid diagrams
    • No < in prose
    • Links use .md and README.md
    • See: docusaurus-conventions skill

Required Metadata Fields

Complete Frontmatter Template

---
id: lesson-{chapter}-{lesson}-{slug}
title: "Lesson {C}.{L}: {Title}"
sidebar_position: {N}
sidebar_label: "{C}.{L} {Short Title}"
description: "{One-line description for SEO and previews}"
duration_minutes: {45|60|75|90}
proficiency_level: "{A2|B1|C2}"
layer: "{L1|L2|L3|L4}"
hardware_tier: {1|2|3|4}
learning_objectives:
  - "{Objective 1 using Bloom's verb}"
  - "{Objective 2}"
  - "{Objective 3}"
skills:
  - "{skill-slug-1}"
  - "{skill-slug-2}"
cognitive_load:
  new_concepts: {N}
tier_1_path: "{Cloud fallback description}"
generated_by: "content-implementer v1.0.0"
created: "{YYYY-MM-DD}"
version: "1.0.0"
---

Field Descriptions

FieldRequiredDescriptionExample
idYesUnique across all docslesson-1-1-digital-to-physical
titleYesFull lesson title"Lesson 1.1: From ChatGPT to Walking Robots"
sidebar_positionYesOrder in sidebar1
sidebar_labelYesShort label for sidebar"1.1 Digital to Physical"
descriptionYesSEO/​preview text"Understanding differences between software and embodied AI"
duration_minutesYesExpected completion time45
proficiency_levelYesCEFR-aligned level"A2", "B1", "C2"
layerYesTeaching layer"L1", "L2", "L3", "L4"
hardware_tierYesMinimum hardware required1 (cloud), 2 (GPU), 3 (Jetson), 4 (robot)
learning_objectivesYesMeasurable outcomes (3-5)Array of strings
skillsYesRelated skill slugs["ros2-fundamentals", "ros2-cli"]
tier_1_pathRecommendedCloud fallback"Cloud ROS 2 (TheConstruct)"
cognitive_load.new_conceptsRecommendedConcept count7
generated_byYesGenerator attribution"content-implementer v1.0.0"
createdYesCreation date"2025-11-29"
versionYesContent version"1.0.0"

Deprecated Fields (Do Not Use)

DeprecatedUse Instead
cefr_levelproficiency_level
durationduration_minutes
estimated_timeduration_minutes
chapter / lessonid with pattern

Principles

Principle 1: Metadata-First Generation

Always generate complete metadata BEFORE content.

The metadata defines:

  • How the lesson appears in navigation
  • What proficiency constraints apply
  • What hardware is required
  • What learning is measured

Principle 2: Layer-Appropriate Structure

L1 (Manual Foundation):

  • Direct explanations with analogies
  • Step-by-step walkthroughs
  • NO AI collaboration yet
  • Heavy scaffolding

L2 (AI Collaboration):

  • Three Roles framework (invisible to student)
  • Bidirectional learning examples
  • "Try With AI" prompts
  • Moderate scaffolding

L3 (Intelligence Design):

  • Create reusable skills/​patterns
  • Encode tacit knowledge
  • Cross-project value
  • Minimal scaffolding

L4 (Spec-Driven):

  • Specification-first approach
  • Compose accumulated intelligence
  • Capstone integration
  • Professional autonomy

Principle 3: Hardware-Aware Content

Every lesson must specify:

  1. Minimum hardware_tier (1-4)
  2. tier_1_path fallback for cloud users
  3. Hardware gates for tier-specific content
**Hardware Tier**: {N} | **Cloud Path**: {fallback description}

Principle 4: Consistent Naming

ID Pattern: lesson-{chapter}-{lesson}-{slug}

  • lesson-1-1-digital-to-physical
  • lesson-3-4-services-parameters
  • lesson-7-3-testing-validation

Sidebar Label Pattern: "{C}.{L} {Short Title}"

  • "1.1 Digital to Physical"
  • "3.4 Services & Parameters"
  • "7.3 Testing Validation"

Lesson Structure Template

---
[Complete frontmatter - see above]
---

# {Lesson Title}

**Duration**: {N} minutes | **Layer**: {L1-L4} | **Tier**: {N} ({description})

{Opening hook - 2-3 paragraphs motivating the topic}

## Learning Objectives

By the end of this lesson, you will be able to:
- {Objective 1}
- {Objective 2}
- {Objective 3}

## {Section 1: Core Content}

{Layer-appropriate content}

### {Subsection}

{Progressive explanation}

## {Section 2: Practice/​Application}

{Hands-on content appropriate to layer}

## {Section 3: Assessment} (optional for L1)

{Understanding validation}

## Try With AI

{AI collaboration prompts - required for L2+}

---

**Previous:** [Link] | **Next:** [Link]

Checklist (Use Before Every Lesson)

Metadata

  • All required fields present
  • id is unique and follows pattern
  • proficiency_level not cefr_level
  • duration_minutes not duration
  • skills array populated
  • hardware_tier specified
  • tier_1_path for tier > 1

Content

  • Opening hook engages reader
  • Learning objectives measurable (Bloom's verbs)
  • Layer-appropriate teaching approach
  • Code examples have outputs (for L2+)
  • "Try With AI" section present (for L2+)

Docusaurus

  • File extension .md
  • No Mermaid diagrams
  • No < in prose
  • Links use .md extension
  • Build passes

Integration

This skill is typically invoked by:

  • content-implementer agent during /​sp.implement
  • Direct lesson creation workflows
  • Module content generation

Dependencies:

  • docusaurus-conventions - File naming and syntax
  • learning-objectives - Bloom's taxonomy alignment
  • assessment-builder - Proficiency-appropriate assessments

Version History

VersionDateChange
0.1.02025-11-28Initial placeholder
1.0.02025-11-29Full implementation with metadata standards from Module 1

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