home / skills / zeeshan080 / ai-native-robotics / layer-definitions

layer-definitions skill

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This skill helps assign L1-L5 pedagogical layers to content, ensuring prerequisites and progression align with AI involvement.

npx playbooks add skill zeeshan080/ai-native-robotics --skill layer-definitions

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: layer-definitions
description: Provide L1-L5 pedagogy layer reference for the AI-Native Robotics Textbook. Use when assigning layers to content, understanding layer requirements, or validating layer progression.
allowed-tools: Read
---

# Layer Definitions

## Instructions

When determining pedagogical layers:

1. Assess the content's AI involvement level
2. Match to the appropriate layer (L1-L5)
3. Ensure prerequisites from lower layers are met
4. Validate layer progression is logical

## Layer Overview

| Layer | Name | AI Involvement | Student Role |
|-------|------|----------------|--------------|
| L1 | Manual | None | Full manual work |
| L2 | Collaboration | Assisted | AI helps after understanding |
| L3 | Intelligence | Templated | Using AI templates/skills |
| L4 | Spec-Driven | Guided | AI generates from specs |
| L5 | Full Autonomy | Autonomous | AI-driven end-to-end |

## Layer Selection Guide

**Choose L1 when:**
- Teaching foundational concepts
- Student must understand without AI assistance
- Building mental models

**Choose L2 when:**
- Student understands the concept
- AI can provide extensions or variations
- Collaboration enhances learning

**Choose L3 when:**
- Teaching reusable patterns
- Introducing AI templates and skills
- Building on L1-L2 understanding

**Choose L4 when:**
- Working with specifications
- AI generates implementation from design
- Integration of multiple components

**Choose L5 when:**
- End-to-end autonomous workflows
- Student orchestrates AI agents
- Capstone projects

## Reference

See [layers.md](layers.md) for detailed layer descriptions.

Overview

This skill provides clear L1–L5 pedagogical layer definitions tailored for the AI-Native Robotics Textbook. It helps instructors assign appropriate layers to lessons, validate progression, and ensure AI involvement matches learning goals. Use it to maintain consistency across curriculum content and to communicate expectations to students.

How this skill works

The skill inspects content for AI involvement, learning objectives, and required student actions, then maps the content to one of five layers: Manual (L1) through Full Autonomy (L5). It verifies that lower-layer prerequisites are present and that the progression from one layer to the next is logical and teachable. The output is a recommended layer with rationale and checklist items for prerequisite knowledge and tooling.

When to use it

Assigning a pedagogical layer to a new lesson, module, or exercise
Validating that course progression enforces prerequisite knowledge
Designing assessments to match expected student roles and AI involvement
Refining project briefs to specify AI autonomy and student responsibilities
Reviewing curriculum for consistent AI-integration across topics

Best practices

Start modules at the lowest layer needed to teach foundational concepts before adding AI tools
Document prerequisites from lower layers explicitly when promoting content to a higher layer
Use concrete rubrics that map student actions to layer expectations (e.g., manual tasks vs. orchestration)
Prefer incremental progression: L1 → L2 → L3 → L4 → L5 rather than jumping layers
Align assessments with the student role defined by the layer (hands-on, assisted, templated, guided, autonomous)

Example use cases

Labeling a lecture on sensor fundamentals as L1 to ensure students build mental models without AI
Classifying a lab where students use AI suggestions as L2 for collaborative exploration
Designing a template-based coding exercise as L3 that teaches reusable AI-driven patterns
Creating a system-integration assignment as L4 where students supply specs and AI generates components
Defining a capstone where teams orchestrate multiple agents as L5 for end-to-end autonomy

FAQ

What distinguishes L3 from L4?

L3 focuses on using AI templates and reusable skills with human guidance; L4 requires students to provide specifications and have AI generate implementations that integrate multiple components.

Can content span multiple layers?

Yes. Complex modules can include activities at different layers; each activity should be labeled and prerequisites enforced so students progress predictably.