Agent skill

layered-first-principles-teaching

Transform complex concepts into progressive, first-principles explanations

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/layered-first-principles-teaching

SKILL.md

Layered First Principles Teaching

Transform complex concepts into progressive, first-principles explanations that build understanding layer by layer.

Quick Start

bash
# Explain a concept progressively
kimi layered-first-principles-teaching "Explain blockchain"

# Target specific audience
kimi layered-first-principles-teaching "Explain transformers" --audience beginner

# Output to file
kimi layered-first-principles-teaching "Explain consensus algorithms" --output ./tutorial.md

Output Structure

Generated explanations contain 6 standard sections:

Section Content Purpose
Opening One-sentence essence + intuitive analogy Immediate understanding
First Principles Problem essence, why existing solutions fail Foundation building
Progressive Layers 3-4 layers from intuition to technical detail Scaffolding learning
Analogies Cross-domain comparisons Relating to known concepts
Visualizations ASCII diagrams, mental models Spatial understanding
Summary Key takeaways + further reading Retention & next steps

Audience Levels

Level Characteristics Approach
Beginner No prior knowledge Heavy analogies, minimal jargon, focus on "why"
Intermediate Some domain knowledge Balance of intuition and technical detail
Expert Deep domain knowledge Focus on nuances, edge cases, implementation

Teaching Patterns

This skill uses progressive disclosure patterns from prompts/:

  1. First Principles Analysis (first_principles.txt): Strip away abstractions, find root causes
  2. Layered Decomposition (layered_decomposition.txt): Break into 3-4 cognitive layers
  3. Analogy Generation (analogy_generation.txt): Find relatable comparisons
  4. Visualization Design (visualization_design.txt): Create mental models and diagrams

Templates

Output templates in templates/ provide structure for:

  • concept.md: General concept explanation
  • algorithm.md: Algorithm walkthrough
  • system.md: System architecture explanation

Examples

See examples/ for completed explanations:

  • blockchain_explained.md: From "digital ledger" to Byzantine fault tolerance
  • transformers_explained.md: From "pattern matching" to attention mechanisms

Workflow

When explaining a concept:

  1. Load first principles prompt → Identify core problem and breakthrough insight
  2. Load layered decomposition prompt → Structure into 3-4 cognitive layers
  3. Load analogy generation prompt → Find 2-3 cross-domain analogies
  4. Load visualization design prompt → Create ASCII diagrams and mental models
  5. Apply appropriate template → Generate final explanation

Constraints

  • Maximum 4 layers to avoid cognitive overload
  • Each layer must build on previous without introducing new prerequisites
  • Analogies must be familiar to target audience
  • Visualizations should work in plain text (ASCII)

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