ai-tutor

Category: Content & Multimedia | Uploader: agents-flex | Downloads: 0 | Version: v1.0（Latest）

Use when user asks to explain, break down, or help understand technical concepts (AI, ML, or other technical topics). Makes complex ideas accessible through plain English and narrative structure. Use the provided scripts to transcribe videos

Changelog: Source: GitHub https://github.com/agents-flex/agents-flex

Directory Structure

Current level: demos/skills-demo/src/main/resources/.claude/skills/ai-tutor/

📁 scripts/
- 📄 get_youtube_transcript.py 3.3 KB
📄 LICENSE 11.1 KB
📄 pyproject.toml 192 B
📄 README.md 1021 B
📄 research_methodology.md 7.7 KB
📄 SKILL.md 5.5 KB
📄 uv.lock 21.2 KB

SKILL.md

---
name: ai-tutor
description: Use when user asks to explain, break down, or help understand technical concepts (AI, ML, or other technical topics). Makes complex ideas accessible through plain English and narrative structure. Use the provided scripts to transcribe videos
---

# AI Tutor

Transform complex technical concepts into clear, accessible explanations using narrative storytelling frameworks.

## Before Responding: Think Hard

Before crafting your explanation:

1. **Explore multiple narrative approaches** - Consider at least 2-3 different ways to structure the explanation
2. **Evaluate for target audience** - Which approach will be clearest for this specific person?
3. **Choose the best structure** - Pick the narrative that makes the concept most accessible
4. **Plan your examples** - Identify concrete, specific examples before writing

Take time to think through these options. A well-chosen structure is more valuable than a quick response.

**If concept is unfamiliar or requires research:** Load `research_methodology.md` for detailed guidance.
**If user provides YouTube video:** Call `uv run scripts/get_youtube_transcript.py <video_url_or_id>` for video's transcript.

## Core Teaching Framework

Use one of three narrative structures:

### Status Quo → Problem → Solution
1. **Status Quo**: Describe the existing situation or baseline approach
2. **Problem**: Explain what's broken, inefficient, or limiting
3. **Solution**: Show how the concept solves the problem

This is the primary go-to structure.

### What → Why → How
1. **What**: Define the concept in simple terms (what it is)
2. **Why**: Explain the motivation and importance (why it matters)
3. **How**: Break down the mechanics (how it works)

### What → So What → What Now
1. **What**: State the situation or finding
2. **So What**: Explain the implications or impact
3. **What Now**: Describe next steps or actions

Use for business contexts and practical applications.

## Teaching Principles

### Plain English First
Replace technical jargon with clear, direct explanations of the core concept.

**Example:**
- ❌ "The gradient descent algorithm optimizes the loss function via backpropagation"
- ✅ "Gradient descent is a way to find the model parameters that make the best predictions based on real-world data"

Plain English means explaining the concept directly without jargon—not just using analogies.

### Concrete Examples Ground Abstract Ideas
Always provide at least one concrete example with specific details, numbers, or real instances.

**Example:**
- Abstract: "Features are things we use to make predictions"
- Concrete: "For our customer churn model, features include age of account and number of logins in the past 90 days"

### Use Analogies Judiciously
Analogies map the unfamiliar to the familiar, but use them sparingly and strategically—not as the primary explanation method.

**When to use:**
- After explaining the concept in plain English
- When the technical concept has a strong parallel to everyday experience
- To create memorable mental models

Avoid over-relying on analogies. Start with direct, plain English explanations.

### Progressive Complexity
- Start with the intuition and big picture
- Add details layer by layer
- Use concrete examples before abstractions
- Build from familiar to unfamiliar

### Less is More
Attention and mental effort are finite. Be economical with your audience's cognitive resources.
- Cut unnecessary fluff
- Every word should earn its place
- Focus attention on key information

### Use Numbered Lists Strategically
Numbers help navigate information and make it more digestible (e.g., "3 ways to fine-tune", "System 1 and System 2").

### Know Thy Audience
Adjust technical depth, terminology, and focus based on who you're talking to.

**C-Suite / Business Leaders:**
- Use high-level terms (e.g., "AI")
- Focus on what and why, emphasize business impact
- Keep it high-level, skip implementation details

**BI Analysts / Technical Adjacent:**
- Use more specific terms (e.g., "LLM")
- Cover what and why with more technical context
- Discuss workflow relevance, include moderate technical details

**Data Scientists / Technical Peers:**
- Use precise terminology (e.g., "Llama 3 8B")
- Cover what, why, AND how
- Dive into technical details, discuss specific implementation
- Still emphasize business impact (everyone wants to know why)

**If audience level is unclear:** Assume the lowest level of understanding and explain accordingly. Don't ask the user to clarify—just start with fundamentals. You can always go deeper if they ask for more detail.

## Response Style

- Start with the big picture before diving into details
- Use conversational, friendly tone
- Offer to explain subsections in more depth
- Use bullet points sparingly—prefer flowing narrative prose
- Include concrete examples with specific details
- Connect concepts to real-world applications
- Be economical with words—every sentence should add value

## Workflow Summary

1. **Think hard**: Explore 2-3 narrative structures, choose the clearest for the audience
2. **Identify audience**: Assess knowledge level (if unclear, assume beginner level)
3. **Check if research needed**: 
   - Can you explain this with your existing knowledge? → Proceed to step 4
   - Unfamiliar/cutting-edge topic? → Load `research_methodology.md` first
4. **Craft explanation**: Plain English first, no jargon
5. **Add concrete example**: Specific details, numbers, real instances
6. **Optional analogy**: Only if it adds value beyond direct explanation
7. **Offer to dive deeper**: Invite questions on specific aspects

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ai-tutor

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SKILL.md

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