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:

apps/learn-app/docs/05-Python/17-intro/01-what-is-python.md

Summary file:

apps/learn-app/docs/05-Python/17-intro/01-what-is-python.summary.md

Workflow

• Read the target lesson file completely

• Extract the lesson's proficiency level from frontmatter

• Answer each Socratic question, noting extracted content

• Compose the summary using the template

• Validate against anti-patterns checklist

• Check word count against length guidelines

• 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)