docs: Update documentation to reflect collaborative workspace enhancements

- Add Collaborative Workspace System and Universal Installer Integration to feature highlights
- Expand BMad Method from two to three key innovations including Collaborative Quality Assurance
- Update guiding principles to emphasize quality-first and collaborative design
- Correct "one-click" to "automated" for accurate installer description
- Add workspace integration and auto-escalation requirements to agent design rules

README.md

@@ -25,6 +25,8 @@ This branch is testing **twelve transformative quality framework enhancements**
 - **📋 Story-to-Code Audit** - Automatic cross-reference between completed stories and actual implementation
 - **🔧 IDE Environment Detection** - Auto-adapt to 8+ IDEs including Cursor, Claude Code, Windsurf, and more
 - **🎛️ Role-Optimized LLM Settings** - Custom temperature and parameters per agent for maximum performance
+- **🤝 Collaborative Workspace System** - Multi-session AI coordination with auto-escalation to external LLMs
+- **📦 Universal Installer Integration** - Automated workspace deployment with IDE-specific configuration
 
 **📄 [View Complete Enhancement Details](enhancements.md)**
 
@@ -36,13 +38,15 @@ This branch is testing **twelve transformative quality framework enhancements**
 
 ## Overview
 
-**BMad Method's Two Key Innovations:**
+**BMad Method's Three Key Innovations:**
 
 **1. Agentic Planning:** Dedicated agents (Analyst, PM, Architect) collaborate with you to create detailed, consistent PRDs and Architecture documents. Through advanced prompt engineering and human-in-the-loop refinement, these planning agents produce comprehensive specifications that go far beyond generic AI task generation.
 
 **2. Context-Engineered Development:** The Scrum Master agent then transforms these detailed plans into hyper-detailed development stories that contain everything the Dev agent needs - full context, implementation details, and architectural guidance embedded directly in story files.
 
-This two-phase approach eliminates both **planning inconsistency** and **context loss** - the biggest problems in AI-assisted development. Your Dev agent opens a story file with complete understanding of what to build, how to build it, and why.
+**3. Collaborative Quality Assurance:** Built-in workspace system enables cross-agent coordination, automatic escalation to external LLMs when stuck, and comprehensive quality validation to prevent technical debt from ambiguous requirements.
+
+This three-phase approach eliminates **planning inconsistency**, **context loss**, and **implementation ambiguity** - the biggest problems in AI-assisted development. Your Dev agent opens a story file with complete understanding of what to build, how to build it, and why, plus automatic collaboration when requirements need clarification.
 
 **📖 [See the complete workflow in the User Guide](bmad-core/user-guide.md)** - Planning phase, development cycle, and all agent roles
 

docs/GUIDING-PRINCIPLES.md

@@ -23,6 +23,14 @@ The BMad Method is a natural language framework for AI-assisted software develop
 - **Tasks are procedures**: Step-by-step instructions an agent follows to complete work
 - **Templates are outputs**: Structured documents with embedded instructions for generation
 - **Dependencies matter**: Explicitly declare only what's needed
+- **Collaborative by design**: Agents support workspace coordination, cross-agent handoffs, and automatic escalation
+
+### 4. Quality and Collaboration First
+
+- **Zero tolerance for simulation**: Automatic detection and prevention of placeholder implementations
+- **Collaborative problem-solving**: Auto-escalation to external LLMs and cross-agent coordination when stuck
+- **Universal IDE support**: Workspace functionality works across all major IDEs and development environments
+- **Intelligent automation**: Automatic loop detection, quality validation, and remediation generation
 
 ## Practical Guidelines
 
@@ -47,6 +55,8 @@ See [Expansion Packs Guide](../docs/expansion-packs.md) for detailed examples an
 1. **Web/Planning Agents**: Can have richer context, multiple tasks, extensive templates
 2. **Dev Agents**: Minimal dependencies, focused on code generation, lean task sets  
 3. **All Agents**: Clear persona, specific expertise, well-defined capabilities
+4. **Workspace Integration**: All agents include collaborative workspace commands for cross-agent coordination
+5. **Auto-Escalation**: Built-in loop detection and external LLM collaboration for complex problems
 
 ### Task Writing Rules
 

enhancements.md

@@ -23,6 +23,8 @@
 | **📋 Story-to-Code Audit** | Ensure completed stories match actual implementation | Auto-cross-reference with gap detection and remediation story generation |
 | **🔧 IDE Environment Detection** | Optimize tool usage based on detected IDE | Auto-adapt to Cursor, Claude Code, Windsurf, Trae, Roo, Cline, Gemini, Copilot |
 | **🚀 Claude Code CLI Optimization** | Premium workspace experience for Claude Code users | Native commands, auto-session management, context-aware handoffs, built-in maintenance |
+| **🤝 Collaborative Workspace System** | Multi-session AI coordination with auto-escalation | Cross-IDE collaboration, automatic external LLM integration, ambiguity resolution |
+| **📦 Universal Installer Integration** | Automated workspace deployment across IDEs | Auto-configures collaborative features based on IDE selection during installation |
 
 ---
 
@@ -33,7 +35,7 @@
 *develop-story        # Systematic story implementation with dual-track progress updates
 *reality-audit        # Comprehensive quality validation with regression analysis  
 *build-context        # Pre-fix investigation with git history and risk assessment
-*escalate            # External AI collaboration when stuck in loops
+*escalate            # Auto-escalation to external LLMs when stuck (3 failed attempts triggers)
 *workspace-init       # Initialize collaborative workspace session (Claude Code CLI)
 *workspace-status     # Show workspace status and collaboration context  
 *workspace-handoff    # Context-aware agent transitions with intelligent suggestions