Module Creation Best Practices

This document is a practical guide for efficiently creating new modules for the AI Instruction Kits.

📄 Original Documents

For detailed content, please refer to the original documents:

• 日本語版
• English Version

🎯 Overview

This document summarizes practical insights gained from a large-scale module creation project in January 2025. It particularly focuses on efficient parallel investigation strategies and quality assurance methods.

🚀 Key Learning Points

1. Power of Parallel Investigation Strategy

Performance Data

• Modules Created: 41 (35 new + 6 improved)
• Work Time: Approximately 3 hours
• Efficiency: Average 4.4 minutes/module

Success Factors

1. Ensuring Task Independence
   • Make each investigation task completely independent
   • Process dependent tasks sequentially
2. Appropriate Tool Selection
   • Web search: Understanding latest trends
   • Literature review: Confirming authoritative sources
   • Implementation example collection: Practical code examples
3. Utilizing Batch Processing
   • Process similar tasks together
   • Minimize context switching

2. Module Development Process

Phase 1: Planning and Preparation

Checklist:
  - Determine category
  - Estimate number of required modules
  - Plan investigation strategy
  - Prepare templates

Phase 2: Parallel Investigation

Investigation items:
  - 2024-2025 best practices
  - Industry standards and specifications
  - Implementation patterns and anti-patterns
  - Tools and frameworks

Phase 3: Implementation

Implementation steps:
  1. Create metadata (YAML)
  2. Write body content (Markdown)
  3. Define variables and dependencies
  4. Add implementation examples

Phase 3.5: Creating Concise Version

Important principles:
  - Create detailed version first: Complete version based on broad and deep research with best practices
  - Extract essence: Extract only the most important concepts from the detailed version
  - Size target: 20-30% of detailed version (focusing on token efficiency)
  
Creation steps:
  1. Confirm detailed version completion
  2. Identify core concepts (express in 1-2 sentences)
  3. Convert to quick reference in tabular format
  4. List essential best practices as bullet points

This order enables creation of a well-founded concise version based on deep understanding.

Phase 4: Quality Assurance

Quality checks:
  - Structural consistency
  - Verify implementation examples work
  - Variable appropriateness
  - Documentation completeness

3. Category-Specific Best Practices

Expertise Modules

• Features: Deep specialized knowledge, emphasis on latest trends
• Required Elements:
  • Theoretical background
  • Implementation examples (3 or more)
  • Compliance with industry standards
  • Success metrics

Skills Modules

• Features: Specific implementation techniques
• Required Elements:
  • Step-by-step guides
  • Error handling
  • Performance considerations
  • Practical tips

Methods Modules

• Features: Processes and methodologies
• Required Elements:
  • Phased approaches
  • Deliverables for each phase
  • Clear roles and responsibilities
  • Implementation examples

📊 Quality Metrics

Quantitative Metrics

coverage:
  - Module coverage: 90% or above
  - Test coverage: 80% or above
  - Documentation completeness: 100%

performance:
  - Generation time: Within 5 seconds
  - Memory usage: 100MB or less
  - Dependency resolution: Automatic

Qualitative Metrics

quality:
  - Readability: Clear and concise
  - Practicality: Immediately usable
  - Maintainability: Easy to update
  - Extensibility: Simple to add new features

🛠️ Recommended Tools and Techniques

Development Tools

1. VS Code: Syntax highlighting and preview
2. Git: Version control
3. Python: Module generation testing
4. YAML Linter: Metadata validation

Efficiency Techniques

1. Template Utilization

   cp templates/module_template.md modules/new_module.md
   

2. Batch Generation Scripts

   # Bulk generation of multiple modules
   for module in module_list:
       generate_module(module)
   

3. Automated Validation Tools

   scripts/validate-modules.sh
   

🔍 Module Validation

Validation Script Overview

The project includes scripts to automatically validate module metadata (YAML files) for correctness.

Usage

# Validate all modules
./scripts/validate-modules.sh

# Example output
🔍 Starting module metadata validation...
📂 Language: en
  📁 Category: tasks
    ✓ blog_writing.yaml
    ⚠ project_planning.yaml
    ✗ thesis_writing.yaml

Validation Items

Required Fields

• id: Module identifier
• name: Module name
• version: Version information
• description: Module description

Format Checks

• Array fields: tags, dependencies, prerequisites must be arrays
• String fields: id, name, description must be strings
• Naming convention: id should start with {category}_ (e.g., task_, skill_)

Common Errors and Solutions

1. Dependencies Field Format Error

# ❌ Incorrect
dependencies:
  required:
    - module_name
  optional:
    - another_module

# ✅ Correct
dependencies:
  - module_name
  - another_module

2. ID Naming Convention Mismatch

# ❌ Incorrect (for tasks category)
id: "project_planning"

# ✅ Correct
id: "task_project_planning"

CI/CD Integration

It’s recommended to run the validation script locally before creating a PR. In the future, automatic validation will be executed via GitHub Actions.

🎓 Learning Resources

Recommended Materials

• Modular System Development Guide
• Sample Modules for Each Category

Community

• GitHub Issues: Questions and discussions
• Pull Requests: Improvement suggestions

🚀 Start Now

1. Copy template
2. Plan investigation
3. Execute parallel investigation
4. Implement module
5. Run validation script

   ./scripts/validate-modules.sh
   

6. Fix any errors
7. Create pull request

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Let's enrich the AI Instruction Kits with efficient module development!