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CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

This is an AI Team Management Best Practices repository containing visual guides, checklists, and interactive tools for implementing AI-augmented development teams. The project helps development teams transition from traditional practices to AI-first methodologies by treating AI agents as specialized team members.

Architecture & Structure

Core Components

Interactive HTML Visualizations (src/):

  • Self-contained HTML files with embedded CSS and JavaScript
  • No build process or dependencies required
  • Open directly in browsers for interactive experiences
  • Each file represents a complete educational module:
    • Mental model transformations (traditional dev → AI agents)
    • Assessment tools with scoring algorithms
    • Interactive checklists with progress tracking
    • Visual guides with animated diagrams

Documentation System (docs/):

  • Follows information-dense keywords (IDK) methodology
  • Hierarchical command dictionary organized by function
  • Research artifacts and consensus findings
  • AI instruction files for consistent assistant behavior

Content Philosophy:

  • Spec-driven development approach
  • Agent orchestration over individual AI usage
  • Cost management as resource allocation
  • Quality assurance integration throughout workflows

Key Architectural Patterns

Information Dense Keywords System:

  • Commands organized in docs/dictionary/ by category (core, development, documentation, quality-assurance, workflow, git)
  • Each command file follows consistent structure: Definition → Usage → Expected Output Format
  • Supports command chaining for complex workflows
  • Reference docs/information-dense-keywords.md for the complete index

Self-Contained Interactive Tools:

  • All HTML files in src/ are complete applications
  • No external dependencies or build systems
  • Embedded styling uses CSS custom properties for theming
  • JavaScript implements scoring, progress tracking, and dynamic content generation

Working with This Codebase

Adding New Interactive Tools

# Create new HTML file in src/
# Follow existing pattern: embedded CSS, self-contained JavaScript
# Include interactive elements (scoring, progress bars, dynamic recommendations)

Updating Documentation

# Follow IDK methodology in docs/dictionary/
# Maintain consistent command structure
# Update docs/information-dense-keywords.md index when adding commands

Content Standards

  • All HTML visualizations must be browser-ready without build steps
  • Documentation follows spec-driven development principles
  • Interactive tools should provide measurable outcomes and actionable recommendations
  • Maintain consistent visual design language across all HTML files

Information Dense Keywords Integration

This repository implements the IDK methodology for AI collaboration. Reference docs/AI.md for complete assistant instructions. Key commands applicable to this project:

  • document this - Generate comprehensive documentation with examples
  • analyze this - Examine code/architecture for patterns and implementation opportunities
  • spec this - Create detailed technical specifications for new features
  • review this - Perform thorough assessment of interactive tools and documentation

Command Chaining Examples

# Sequential development workflow
analyze this assessment tool then spec this improvement then document this change

# Parallel quality assurance  
test this interactive HTML and review this documentation

Project-Specific Considerations

Mental Model Focus: The project emphasizes treating AI as specialized team members rather than tools. When adding content, maintain this perspective throughout all materials.

Measurement-Driven: All interactive tools should provide quantifiable assessments, progress tracking, and concrete metrics. Avoid vague qualitative measures.

Implementation-Ready: Content should bridge the gap between concept and practice. Include specific templates, checklists, and actionable frameworks rather than theoretical discussions.

Visual Learning: Leverage interactive diagrams, progress indicators, and visual metaphors to communicate complex orchestration concepts effectively.