summary-generator

name	summary-generator
description	This skill should be used when generating lesson summaries for educational content. It extracts key concepts, mental models, patterns, and common mistakes from lesson markdown files using a Socratic extraction process. Use this skill when a lesson file needs a `.summary.md` companion file, or when reviewing/refreshing existing summaries.

Summary Generator

Overview

This skill generates concise, scannable summaries for educational lessons by extracting the essential learning elements through Socratic questioning. Summaries serve two user needs: quick review (students returning to refresh understanding) and just-in-time reference (students checking back mid-practice).

Extraction Process (Socratic Style)

To generate a summary, work through these questions in order. Each question extracts content for one section of the summary.

Question 1: Core Concept

"If a student remembers only ONE thing from this lesson tomorrow, what must it be?"

Extract the single most important takeaway in 1-2 sentences. This should be the foundational insight that unlocks everything else.

Test: Could someone who only read this sentence explain the lesson's purpose to a peer?

Question 2: Key Mental Models

"What mental frameworks does this lesson install in the student's mind? What 'lenses' do they now see problems through?"

Extract 2-3 mental models—these are the reusable thinking patterns, not facts. Look for:

• Cause → Effect relationships
• Decision frameworks ("When X, do Y")
• Conceptual metaphors or analogies

Test: Are these transferable to new situations, or are they lesson-specific facts?

Question 3: Critical Patterns

"What practical techniques or patterns does this lesson teach? What can the student now DO that they couldn't before?"

Extract 2-4 actionable patterns from the lesson. These come from:

• Code examples and their purpose
• AI collaboration techniques
• Tools or commands introduced
• Workflows demonstrated

Test: Could a student apply these patterns without re-reading the lesson?

Question 4: AI Collaboration Keys

"How does AI help with this topic? What prompts or collaboration patterns make the difference?"

Extract 1-2 insights about working with AI on this topic. This should NOT expose the Three Roles framework—focus on practical collaboration patterns.

Note: Skip this section if the lesson doesn't involve AI collaboration (Layer 1 content).

Question 5: Common Mistakes

"Where do students typically go wrong? What misconceptions does this lesson correct?"

Extract 2-3 common mistakes from:

• Explicit "Common Mistakes" sections
• Error examples in the lesson
• Counterintuitive points that contradict assumptions

Test: Would knowing these prevent a real mistake?

Question 6: Connections

"What prerequisite knowledge does this build on? Where does this lead next?"

Extract navigation links:

• Builds on: What prior concepts are assumed
• Leads to: What this enables in future lessons

Note: This section is optional. Skip if connections aren't clear or useful.

Output Template

Generate the summary following this exact structure:

### Core Concept
[1-2 sentences from Question 1]

### Key Mental Models
- **[Model Name]**: [Brief explanation]
- **[Model Name]**: [Brief explanation]
- **[Model Name if needed]**: [Brief explanation]

### Critical Patterns
- [Pattern/technique 1]
- [Pattern/technique 2]
- [Pattern/technique 3 if applicable]
- [AI collaboration pattern if applicable]

### Common Mistakes
- [Mistake 1 and why it's wrong]
- [Mistake 2 and why it's wrong]
- [Mistake 3 if applicable]

### Connections
- **Builds on**: [Prior concept/chapter]
- **Leads to**: [Next concept/chapter]

Length Guidelines

Adjust summary length based on lesson complexity (from frontmatter proficiency_level):

Proficiency	Target Length	Reason
A1-A2 (Beginner)	150-250 words	Simpler concepts, fewer patterns
B1-B2 (Intermediate)	200-350 words	More nuanced, multiple techniques
C1-C2 (Advanced)	250-400 words	Complex topics, many interconnections

Anti-Patterns (What NOT to Include)

Following Principle 7: Minimal Sufficient Content, summaries must NOT contain:

• ❌ Full explanations — Summaries point to concepts, not re-teach them
• ❌ Code examples — The full lesson contains these
• ❌ Practice exercises — Students return to the lesson for practice
• ❌ "What's Next" navigation — Course structure handles this
• ❌ Motivational content — No "Congratulations!" or fluff
• ❌ Layer/Stage labels — Students experience pedagogy, not study it
• ❌ Framework terminology — No "Three Roles", "Layer 2", etc.

File Naming Convention

Summary files are named by appending .summary.md to the lesson filename (without extension):

# Lesson file:
robolearn-interface/docs/05-Python/17-intro/01-what-is-python.md

# Summary file:
robolearn-interface/docs/05-Python/17-intro/01-what-is-python.summary.md

Workflow

1. Read the target lesson file completely
2. Extract the lesson's proficiency level from frontmatter
3. Answer each Socratic question, noting extracted content
4. Compose the summary using the template
5. Validate against anti-patterns checklist
6. Check word count against length guidelines
7. Write the .summary.md file

Example: Data Types Lesson Summary

For a lesson teaching Python data types at A2 proficiency:

### Core Concept
Data types are Python's classification system—they tell Python "what kind of data is this?" and "what operations are valid?"

### Key Mental Models
- **Types → Operations**: Numbers enable math; text enables joining; booleans enable decisions
- **Type Mismatch → Error**: `5 + "hello"` fails because Python can't add numbers to text
- **Type Decision Framework**: Ask "What kind of data?" to determine the right type

### Critical Patterns
- Use `type()` to verify what type Python assigned: `type(42)` returns `<class 'int'>`
- Type hints express intent: `age: int = 25` tells both AI and humans what you expect
- 7 categories cover all data: Numeric, Text, Boolean, Collections, Binary, Special (None)

### Common Mistakes
- Storing numbers as text (`"25"` instead of `25`) prevents math operations
- Forgetting that `0.1 + 0.2` doesn't exactly equal `0.3` (floating point precision)
- Mixing types in operations without explicit conversion

### Connections
- **Builds on**: Python installation and first programs (Chapter 17)
- **Leads to**: Deep dive into numeric types and text handling (Chapters 18-20)

Word count: ~175 words (appropriate for A2)