Agent skill
layered-first-principles-teaching
Transform complex concepts into progressive, first-principles explanations
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
# 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/:
- First Principles Analysis (
first_principles.txt): Strip away abstractions, find root causes - Layered Decomposition (
layered_decomposition.txt): Break into 3-4 cognitive layers - Analogy Generation (
analogy_generation.txt): Find relatable comparisons - Visualization Design (
visualization_design.txt): Create mental models and diagrams
Templates
Output templates in templates/ provide structure for:
concept.md: General concept explanationalgorithm.md: Algorithm walkthroughsystem.md: System architecture explanation
Examples
See examples/ for completed explanations:
blockchain_explained.md: From "digital ledger" to Byzantine fault tolerancetransformers_explained.md: From "pattern matching" to attention mechanisms
Workflow
When explaining a concept:
- Load first principles prompt → Identify core problem and breakthrough insight
- Load layered decomposition prompt → Structure into 3-4 cognitive layers
- Load analogy generation prompt → Find 2-3 cross-domain analogies
- Load visualization design prompt → Create ASCII diagrams and mental models
- 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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