Add standardized module introduction template to agents

- Created consistent module introduction format
- Updated Module Developer agent with mandatory template
- Updated Documentation Publisher agent with same template
- Ensures all modules follow same structure:
  - Welcome statement
  - 5 Learning Goals (systems-focused)
  - Build → Use → Reflect pattern
  - What You'll Achieve section
  - Systems Reality Check section
- Focus on systems thinking, performance, and production relevance

.claude/agents/documentation-publisher.md

@@ -32,11 +32,54 @@ You are an expert in creating, writing, and publishing educational CONTENT for M
 
 ### 1. **Content Creation & Writing**
 Create engaging, educational content that explains complex concepts:
-- Module introductions and explanations
+- Module introductions following the STANDARDIZED format (see below)
 - Concept descriptions and examples
 - Learning objectives and outcomes
 - Educational narratives and stories
 
+**STANDARDIZED MODULE INTRODUCTION FORMAT (MANDATORY):**
+Every module introduction MUST follow this exact template:
+
+```python
+"""
+# [Module Name] - [Descriptive Subtitle]
+
+Welcome to the [Module Name] module! [One exciting sentence about what students will achieve/learn].
+
+## Learning Goals
+- [Systems understanding - memory/performance/scaling focus]
+- [Core implementation skill they'll master]
+- [Pattern/abstraction they'll understand]
+- [Framework connection to PyTorch/TensorFlow]
+- [Optimization/trade-off understanding]
+
+## Build → Use → Reflect
+1. **Build**: [What they implement from scratch]
+2. **Use**: [Real application with real data/problems]
+3. **Reflect**: [Systems thinking question about performance/scaling/trade-offs]
+
+## What You'll Achieve
+By the end of this module, you'll understand:
+- [Deep technical understanding gained]
+- [Practical capability developed]
+- [Systems insight achieved]
+- [Performance consideration mastered]
+- [Connection to production ML systems]
+
+## Systems Reality Check
+💡 **Production Context**: [How this is used in real ML systems like PyTorch/TensorFlow]
+⚡ **Performance Note**: [Key performance insight, bottleneck, or optimization to understand]
+"""
+```
+
+**Introduction Rules:**
+- Always use "Build → Use → Reflect" (never "Understand" or "Analyze")
+- Always use "What You'll Achieve" (never "What You'll Learn")
+- Always include exactly 5 learning goals with specified focus areas
+- Always include "Systems Reality Check" section
+- Keep friendly "Welcome to..." opening
+- Focus on systems thinking, performance, and production relevance
+
 ### 2. **ML Systems Thinking Questions**
 Develop interactive assessment content:
 - Systems-focused reflection questions

.claude/agents/module-developer.md

@@ -30,6 +30,50 @@ You MUST use the 10-Part structure defined in MODULE_STANDARD_TEMPLATE.md:
 
 CRITICAL: Use these exact part numbers and names for consistency!
 
+### Standardized Module Introduction (MANDATORY)
+
+EVERY module MUST begin with this exact format for the introduction:
+
+```python
+"""
+# [Module Name] - [Descriptive Subtitle]
+
+Welcome to the [Module Name] module! [One exciting sentence about what students will achieve/learn].
+
+## Learning Goals
+- [Systems understanding - memory/performance/scaling focus]
+- [Core implementation skill they'll master]
+- [Pattern/abstraction they'll understand]
+- [Framework connection to PyTorch/TensorFlow]
+- [Optimization/trade-off understanding]
+
+## Build → Use → Reflect
+1. **Build**: [What they implement from scratch]
+2. **Use**: [Real application with real data/problems]
+3. **Reflect**: [Systems thinking question about performance/scaling/trade-offs]
+
+## What You'll Achieve
+By the end of this module, you'll understand:
+- [Deep technical understanding gained]
+- [Practical capability developed]
+- [Systems insight achieved]
+- [Performance consideration mastered]
+- [Connection to production ML systems]
+
+## Systems Reality Check
+💡 **Production Context**: [How this is used in real ML systems like PyTorch/TensorFlow]
+⚡ **Performance Note**: [Key performance insight, bottleneck, or optimization to understand]
+"""
+```
+
+**IMPORTANT RULES for Module Introductions:**
+1. Always use "Build → Use → Reflect" (not "Understand" or "Analyze")
+2. Always use "What You'll Achieve" (not "What You'll Learn")
+3. Always include exactly 5 learning goals with the specified focus areas
+4. Always include the "Systems Reality Check" section
+5. Keep the friendly "Welcome to..." opening
+6. Focus on systems thinking, performance, and production relevance
+
 ## Critical Knowledge - MUST READ
 
 ### NBGrader Integration (CRITICAL)