# Chat App User Stories - Terminal-Based LLM Interface

**Document Version**: 1.0  
**Creation Date**: 2025-09-09  
**Created By**: Claude Code  
**Project**: Kollabor CLI Interface  

---

## Executive Summary

This document provides comprehensive user stories for the Chat App, a terminal-based LLM interface built on a universal hook system. The application represents a foundational platform where "everything has a hook" - every action, from API calls to key presses, triggers customizable hooks that plugins can attach to. This design enables unprecedented customization and extensibility in LLM terminal interfaces.

**Current Status**: Foundational implementation complete with comprehensive plugin architecture  
**Next Phase**: Full requirements document (req.md) implementation  

---

## User Story Index

### **IMPLEMENTED FEATURES** (Current Implementation)

#### Core Terminal Interface
- [US-2025-001](#us-2025-001) - Basic Terminal Chat Interface
- [US-2025-002](#us-2025-002) - Real-time Terminal Rendering
- [US-2025-003](#us-2025-003) - Visual Effects System
- [US-2025-004](#us-2025-004) - Input Buffer Management

#### Hook & Event System
- [US-2025-005](#us-2025-005) - Universal Hook System
- [US-2025-006](#us-2025-006) - Event Bus Architecture
- [US-2025-007](#us-2025-007) - Hook Priority Management
- [US-2025-008](#us-2025-008) - Hook Status Monitoring

#### Plugin System
- [US-2025-009](#us-2025-009) - Plugin Discovery & Loading
- [US-2025-010](#us-2025-010) - Dynamic Plugin Configuration
- [US-2025-011](#us-2025-011) - Plugin Lifecycle Management
- [US-2025-012](#us-2025-012) - Plugin Status Display

#### Core LLM System
- [US-2025-013](#us-2025-013) - Core LLM Communication
- [US-2025-014](#us-2025-014) - Thinking Tag Processing
- [US-2025-015](#us-2025-015) - Conversation Management
- [US-2025-016](#us-2025-016) - LLM Response Processing

#### Configuration & State
- [US-2025-017](#us-2025-017) - Configuration Management
- [US-2025-018](#us-2025-018) - State Persistence
- [US-2025-019](#us-2025-019) - Runtime Configuration Updates

### **PLANNED FEATURES** (From req.md)

#### Advanced Multi-Model Support
- [US-2025-020](#us-2025-020) - Multi-Model Routing
- [US-2025-021](#us-2025-021) - Intelligent Model Selection
- [US-2025-022](#us-2025-022) - Model Collaboration Features

#### MCP Integration
- [US-2025-023](#us-2025-023) - MCP Server Integration
- [US-2025-024](#us-2025-024) - MCP Tool Discovery
- [US-2025-025](#us-2025-025) - MCP-Based Plugin Bridge

#### Advanced Terminal Features
- [US-2025-026](#us-2025-026) - Advanced Status Areas
- [US-2025-027](#us-2025-027) - Plugin Display Sections
- [US-2025-028](#us-2025-028) - Enhanced Visual Effects

#### Developer Experience
- [US-2025-029](#us-2025-029) - Plugin Development SDK
- [US-2025-030](#us-2025-030) - Hot-Reload Plugin Development
- [US-2025-031](#us-2025-031) - Plugin Security Sandbox

#### System Administration
- [US-2025-032](#us-2025-032) - Advanced Monitoring
- [US-2025-033](#us-2025-033) - Performance Optimization
- [US-2025-034](#us-2025-034) - Production Deployment

### **CRITICAL FOUNDATION STORIES** (Stability & Navigation)

#### System Stability & Performance
- [US-2025-035](#us-2025-035) - System Stability and Performance Optimization
- [US-2025-036](#us-2025-036) - Plugin Isolation and Error Recovery
- [US-2025-037](#us-2025-037) - Adaptive Performance Rendering

#### Interactive Terminal Navigation
- [US-2025-038](#us-2025-038) - Multi-row Terminal Navigation System
- [US-2025-039](#us-2025-039) - Interactive Selection and Input Management
- [US-2025-040](#us-2025-040) - Advanced Status Area Layout System

#### Core System Features
- [US-2025-041](#us-2025-041) - Conversation History and Prompt Storage
- [US-2025-042](#us-2025-042) - Slash Commands Core System
- [US-2025-043](#us-2025-043) - Security and Command Restriction System
- [US-2025-044](#us-2025-044) - Multi-line Input Expansion
- [US-2025-045](#us-2025-045) - Enhanced Model Routing System
- [US-2025-046](#us-2025-046) - Plugin Management via Slash Commands
- [US-2025-047](#us-2025-047) - Command-line Integration System
- [US-2025-048](#us-2025-048) - Plugin Charm Icons and Visual Identity
- [US-2025-049](#us-2025-049) - Agent Communication and Management System

---

# IMPLEMENTED USER STORIES

## US-2025-001
### Basic Terminal Chat Interface

```yaml
Story_Metadata:
  Story_ID: "US-2025-001"
  Epic_ID: "EPIC-TERMINAL-INTERFACE"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Done"
  Priority: "Critical"
  Size_Estimate: "8"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "7"
  Risk_Level: "Low"
  Dependencies_Identified: "None - foundational feature"
  Similar_Stories: "None"
  Implementation_Suggestions: "Completed in core.application and main.py"
```

#### User Story Statement

**As a** terminal user  
**I want** to interact with LLM models through a natural terminal interface  
**So that** I can have conversations with AI while maintaining my terminal-based workflow  

#### Story Context
Terminal users prefer command-line interfaces that integrate seamlessly with their existing workflow. They need an LLM chat interface that feels natural in the terminal environment, without clearing the screen or disrupting the terminal history.

#### User Personas
```yaml
Primary_Persona:
  Name: "Devon the Developer"
  Role: "Software Developer"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Lives in the terminal for 80% of work
    - Values efficiency and keyboard-driven interfaces
    - Expects natural terminal flow without screen clearing
  Goals_and_Motivations:
    - Quick access to AI assistance without context switching
    - Integration with existing terminal workflow
  Pain_Points:
    - GUI applications break terminal flow
    - Screen-clearing applications disrupt work context
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Start chat interface"
      Given: "User runs 'python main.py' in terminal"
      When: "Application starts"
      Then: "Shows startup sequence without clearing screen"
      And: "Displays input prompt ready for user input"
      
    Scenario_2:
      Title: "Send message to LLM"
      Given: "Chat interface is running"
      When: "User types message and presses enter"
      Then: "Message appears in terminal conversation flow"
      And: "LLM response appears below user message"
      
  Edge_Case_Scenarios:
    Scenario_3:
      Title: "Handle empty input"
      Given: "Input prompt is active"
      When: "User presses enter without typing"
      Then: "System ignores empty input and maintains prompt"
      
  Error_Scenarios:
    Scenario_4:
      Title: "Handle LLM connection failure"
      Given: "User sends message"
      When: "LLM API is unavailable"
      Then: "Displays clear error message"
      And: "Maintains terminal interface for retry"
```

#### Technical Implementation
```yaml
Implementation_Status:
  Files_Implemented:
    - "/Users/malmazan/dev/chat_app/main.py": "Application entry point"
    - "/Users/malmazan/dev/chat_app/core/application.py": "TerminalLLMChat orchestrator"
    - "/Users/malmazan/dev/chat_app/core/io/terminal_renderer.py": "Main rendering system"
    - "/Users/malmazan/dev/chat_app/core/io/input_handler.py": "Keyboard input processing"
  
  Key_Features_Delivered:
    - Natural terminal flow without screen clearing
    - Real-time input rendering at 20 FPS
    - Async architecture prevents blocking
    - Proper terminal state management
```

---

## US-2025-002
### Real-time Terminal Rendering

```yaml
Story_Metadata:
  Story_ID: "US-2025-002"
  Epic_ID: "EPIC-TERMINAL-INTERFACE"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Done"
  Priority: "High"
  Size_Estimate: "13"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "8"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-001 (Basic Terminal Interface)"
  Similar_Stories: "Modern terminal applications with live updates"
  Implementation_Suggestions: "Implemented via modular I/O system with 11 components"
```

#### User Story Statement

**As a** terminal user  
**I want** the interface to update in real-time without flickering or lag  
**So that** I have a smooth, responsive experience while typing and viewing responses  

#### Story Context
Terminal interfaces traditionally update line-by-line, but modern users expect real-time responsiveness similar to GUI applications. This requires sophisticated rendering that updates only changed regions while maintaining terminal compatibility.

#### User Personas
```yaml
Primary_Persona:
  Name: "Alex the Power User"
  Role: "Technical Writer / Power User"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Fast typist expecting immediate visual feedback
    - Works on high-resolution terminal displays
    - Sensitive to UI performance issues
  Goals_and_Motivations:
    - Immediate feedback for all interactions
    - Professional-quality visual experience
  Pain_Points:
    - Flickering or laggy terminal interfaces
    - Delayed visual feedback while typing
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Real-time typing feedback"
      Given: "User is typing in input field"
      When: "Characters are entered"
      Then: "Characters appear immediately without delay"
      And: "No flickering or visual artifacts occur"
      
    Scenario_2:
      Title: "Smooth status updates"
      Given: "LLM is processing a request"
      When: "Status changes occur"
      Then: "Status areas update smoothly"
      And: "Other screen areas remain stable"
      
  Performance_Scenarios:
    Scenario_3:
      Title: "Maintain 20 FPS rendering"
      Given: "System is under normal load"
      When: "Rendering loop is active"
      Then: "Achieves consistent 20 FPS update rate"
      And: "CPU usage remains reasonable"
```

#### Technical Implementation
```yaml
Implementation_Status:
  Files_Implemented:
    - "/Users/malmazan/dev/chat_app/core/io/terminal_renderer.py": "20 FPS render loop"
    - "/Users/malmazan/dev/chat_app/core/io/visual_effects.py": "Gradient and shimmer effects"
    - "/Users/malmazan/dev/chat_app/core/io/layout.py": "Screen layout management"
    - "/Users/malmazan/dev/chat_app/core/io/buffer_manager.py": "Input buffer with history"
  
  Key_Features_Delivered:
    - 20 FPS render loop with async architecture
    - Dirty region tracking for performance
    - Modular I/O system with 11 specialized components
    - Double-buffering for smooth animations
    - Configurable render settings (terminal.render_fps)
```

---

## US-2025-003
### Visual Effects System

```yaml
Story_Metadata:
  Story_ID: "US-2025-003"
  Epic_ID: "EPIC-TERMINAL-INTERFACE"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Done"
  Priority: "Medium"
  Size_Estimate: "5"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "6"
  Risk_Level: "Low"
  Dependencies_Identified: "US-2025-002 (Real-time Terminal Rendering)"
  Similar_Stories: "Rich terminal libraries, modern CLI tools"
  Implementation_Suggestions: "Implemented in visual_effects.py with configurable effects"
```

#### User Story Statement

**As a** terminal user  
**I want** visually appealing effects like shimmer and gradients for thinking text  
**So that** I can easily distinguish different states and have an enhanced visual experience  

#### Story Context
Modern terminal applications benefit from subtle visual enhancements that provide better user feedback without being overwhelming. Thinking animations help users understand when the AI is processing, while color coding helps organize information visually.

#### User Personas
```yaml
Primary_Persona:
  Name: "Sam the UX-Conscious Developer"
  Role: "Frontend Developer"
  Experience_Level: "Intermediate"
  Key_Characteristics:
    - Appreciates good visual design even in CLI tools
    - Values clear visual feedback for system states
    - Prefers customizable interface elements
  Goals_and_Motivations:
    - Clear visual indication of AI thinking/processing
    - Aesthetically pleasing terminal experience
    - Ability to customize visual preferences
  Pain_Points:
    - Plain text interfaces lack visual hierarchy
    - Difficulty distinguishing system states
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Shimmer effect on thinking text"
      Given: "LLM is processing with thinking tags"
      When: "Thinking text is displayed"
      Then: "Text shows elegant shimmer wave effect"
      And: "Effect moves smoothly across characters"
      
    Scenario_2:
      Title: "Color-coded status information"
      Given: "Status areas are displaying information"
      When: "Different types of data are shown"
      Then: "Numbers appear in cyan, active states in yellow"
      And: "Time measurements appear in magenta"
      
  Configuration_Scenarios:
    Scenario_3:
      Title: "Disable visual effects"
      Given: "User prefers minimal visual effects"
      When: "terminal.thinking_effect is set to 'normal'"
      Then: "No shimmer or special effects are applied"
      And: "Text displays in standard terminal format"
```

#### Technical Implementation
```yaml
Implementation_Status:
  Files_Implemented:
    - "/Users/malmazan/dev/chat_app/core/io/visual_effects.py": "Effect system implementation"
    - "/Users/malmazan/dev/chat_app/core/io/status_renderer.py": "Status color management"
    - "/Users/malmazan/dev/chat_app/core/io/layout.py": "Thinking animation integration"
  
  Key_Features_Delivered:
    - Shimmer effect with configurable speed and width
    - Semantic color system for status information  
    - Dim effects for subtle text appearance
    - Gradient system for smooth color transitions
    - Configuration options: "shimmer", "dim", "normal"
    - 3-frame speed, 4-character wave width parameters
```

---

## US-2025-005
### Universal Hook System

```yaml
Story_Metadata:
  Story_ID: "US-2025-005"
  Epic_ID: "EPIC-HOOK-SYSTEM"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Done"
  Priority: "Critical"
  Size_Estimate: "21"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "9"
  Risk_Level: "High"
  Dependencies_Identified: "Core architecture foundation"
  Similar_Stories: "WordPress hooks, Git hooks, Electron hooks"
  Implementation_Suggestions: "Implemented via EventBus with comprehensive hook lifecycle"
```

#### User Story Statement

**As a** plugin developer  
**I want** to hook into any system action (API calls, user input, responses, tool calls)  
**So that** I can customize, monitor, or extend any aspect of LLM interaction  

#### Story Context
The fundamental architecture principle is "everything has a hook." This means every action in the system - from key presses to API responses - triggers hooks that plugins can attach to. This enables unprecedented customization without modifying core code.

#### User Personas
```yaml
Primary_Persona:
  Name: "Maya the Plugin Developer"
  Role: "Plugin Developer / System Integrator"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Creates custom integrations and automations
    - Needs to intercept and modify system behavior
    - Values comprehensive hook coverage
  Goals_and_Motivations:
    - Ability to customize any aspect of LLM interaction
    - Build complex workflows with multiple integrations
    - Create reusable plugins for different scenarios
  Pain_Points:
    - Limited customization in existing LLM tools
    - Need to modify core code for extensions
    - Lack of comprehensive hook points

Secondary_Personas:
  - Name: "Jordan the System Administrator"
    Role: "DevOps Engineer"
    Relevance: "Needs monitoring and automation hooks"
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Hook into user input"
      Given: "Plugin registers for USER_INPUT events"
      When: "User types and presses enter"
      Then: "Plugin receives pre and post hooks for the input"
      And: "Plugin can modify input before LLM processing"
      
    Scenario_2:
      Title: "Hook into LLM responses"
      Given: "Plugin registers for LLM_RESPONSE events"
      When: "LLM returns a response"
      Then: "Plugin receives response data in post-hook"
      And: "Plugin can transform response before display"
      
    Scenario_3:
      Title: "Hook priority system"
      Given: "Multiple plugins register for same event"
      When: "Event is triggered"
      Then: "Hooks execute in priority order (1000 to 1)"
      And: "Higher priority hooks can cancel event chain"
      
  Edge_Case_Scenarios:
    Scenario_4:
      Title: "Hook cancels event chain"
      Given: "Security hook validates input"
      When: "Hook detects malicious input"
      Then: "Hook sets event.cancelled = True"
      And: "Remaining hooks in chain are skipped"
      
  Error_Scenarios:
    Scenario_5:
      Title: "Hook timeout handling"
      Given: "Hook is processing an event"
      When: "Hook exceeds 30 second timeout"
      Then: "Hook is cancelled and marked as TIMEOUT"
      And: "Event chain continues with remaining hooks"
```

#### Technical Implementation
```yaml
Implementation_Status:
  Files_Implemented:
    - "/Users/malmazan/dev/chat_app/core/events/bus.py": "Central EventBus implementation"
    - "/Users/malmazan/dev/chat_app/core/events/models.py": "Event, Hook, and priority definitions"
    - "/Users/malmazan/dev/chat_app/core/events/executor.py": "Hook execution engine"
    - "/Users/malmazan/dev/chat_app/core/events/processor.py": "Event processing pipeline"
  
  Key_Features_Delivered:
    - Universal hook points for all system actions
    - Priority-based hook execution (1000 = system, 10 = display)
    - Hook status tracking (PENDING, WORKING, COMPLETED, FAILED, TIMEOUT)
    - Event cancellation capability
    - Async hook execution prevents blocking
    - Pre/post hook patterns for all events
    - Hook error handling with configurable actions
    - Timeout management with graceful fallback
```

---

## US-2025-009
### Plugin Discovery & Loading

```yaml
Story_Metadata:
  Story_ID: "US-2025-009"
  Epic_ID: "EPIC-PLUGIN-SYSTEM"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Done"
  Priority: "High"
  Size_Estimate: "8"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "7"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-005 (Universal Hook System)"
  Similar_Stories: "VSCode extension loading, WordPress plugin discovery"
  Implementation_Suggestions: "Implemented in core.plugins with dynamic discovery"
```

#### User Story Statement

**As a** system administrator  
**I want** plugins to be automatically discovered and loaded from the plugins directory  
**So that** I can easily add new functionality without manual registration  

#### Story Context
Plugin systems should be as friction-free as possible. Users should be able to drop Python files into a plugins directory and have them automatically discovered, loaded, and integrated into the system.

#### User Personas
```yaml
Primary_Persona:
  Name: "Riley the System Administrator"
  Role: "DevOps / System Administrator"
  Experience_Level: "Intermediate"
  Key_Characteristics:
    - Manages multiple systems with different plugin needs
    - Prefers automatic discovery over manual configuration
    - Values reliable plugin loading and error handling
  Goals_and_Motivations:
    - Easy plugin deployment and management
    - Reliable system startup with plugin integration
    - Clear feedback on plugin loading status
  Pain_Points:
    - Manual plugin registration is error-prone
    - Need visibility into plugin loading success/failure
    - Difficult to troubleshoot plugin loading issues
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Automatic plugin discovery"
      Given: "Python files exist in plugins/ directory"
      When: "Application starts"
      Then: "All valid plugins are automatically discovered"
      And: "Plugin discovery status is displayed during startup"
      
    Scenario_2:
      Title: "Plugin loading with configuration"
      Given: "Valid plugins are discovered"
      When: "Plugin loading process runs"
      Then: "Each plugin is loaded with its default config"
      And: "Plugin configurations are merged into main config"
      
    Scenario_3:
      Title: "Plugin lifecycle management"
      Given: "Plugins are loaded successfully"
      When: "System initializes"
      Then: "Plugin initialize() methods are called"
      And: "Plugin hooks are registered with event bus"
      
  Error_Scenarios:
    Scenario_4:
      Title: "Handle plugin loading errors"
      Given: "A plugin has syntax or import errors"
      When: "Plugin loading is attempted"
      Then: "Error is logged but system continues"
      And: "Other plugins load successfully"
```

#### Technical Implementation
```yaml
Implementation_Status:
  Files_Implemented:
    - "/Users/malmazan/dev/chat_app/core/plugins/registry.py": "Plugin discovery system"
    - "/Users/malmazan/dev/chat_app/core/plugins/factory.py": "Plugin instantiation"
    - "/Users/malmazan/dev/chat_app/core/plugins/discovery.py": "Dynamic plugin scanning"
    - "/Users/malmazan/dev/chat_app/plugins/__init__.py": "Plugin directory structure"
  
  Key_Features_Delivered:
    - Automatic scanning of plugins/ directory
    - Dynamic Python module loading
    - Plugin validation and error handling
    - Configuration merging from plugin defaults
    - Lifecycle management (initialize, register_hooks, shutdown)
    - Status display during startup sequence
    - Plugin isolation and error recovery
```

---

## US-2025-013
### Core LLM Communication System

```yaml
Story_Metadata:
  Story_ID: "US-2025-013"
  Epic_ID: "EPIC-CORE-LLM-SYSTEM"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Done"
  Priority: "Critical"
  Size_Estimate: "13"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "8"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-005 (Hook System), Core architecture"
  Similar_Stories: "OpenAI API clients, LangChain integrations"
  Implementation_Suggestions: "Implemented as core system in core/llm/ (migrated from plugin)"
```

#### User Story Statement

**As a** end user  
**I want** to send messages to LLM models and receive responses  
**So that** I can have natural conversations with AI through the terminal interface  

#### Story Context
The core functionality of the chat application is communicating with LLM models. This should feel natural and responsive, with proper error handling and status feedback. **Architectural Decision**: LLM functionality is now implemented as a core system component (core/llm/) rather than a plugin, as it's essential functionality that other components depend on. The hook system still allows customization of LLM behavior.

#### User Personas
```yaml
Primary_Persona:
  Name: "Chris the AI Enthusiast"
  Role: "Researcher / Content Creator"
  Experience_Level: "Beginner"
  Key_Characteristics:
    - New to terminal interfaces but eager to use AI tools
    - Expects conversational interaction similar to web interfaces
    - Needs clear feedback on AI processing status
  Goals_and_Motivations:
    - Easy access to powerful AI models
    - Natural conversation flow
    - Understanding of AI processing stages
  Pain_Points:
    - Unclear when AI is processing vs. idle
    - No indication of connection or processing issues
    - Difficulty understanding AI capabilities
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Send message and receive response"
      Given: "LLM API is available at localhost:1234"
      When: "User types message and presses enter"
      Then: "Message is sent to LLM API"
      And: "LLM response appears in conversation"
      
    Scenario_2:
      Title: "Processing status feedback"
      Given: "User sends message to LLM"
      When: "LLM is processing the request"
      Then: "Status area shows 'Processing: Yes'"
      And: "Thinking animation appears if applicable"
      
    Scenario_3:
      Title: "Conversation history management"
      Given: "User has ongoing conversation"
      When: "New messages are exchanged"
      Then: "Conversation context is maintained"
      And: "History limit is enforced (max 20 messages)"
      
  Error_Scenarios:
    Scenario_4:
      Title: "Handle API connection failure"
      Given: "LLM API is unavailable"
      When: "User sends message"
      Then: "Clear error message is displayed"
      And: "User can retry when service is restored"
```

#### Technical Implementation
```yaml
Implementation_Status:
  Files_To_Migrate:
    - "/Users/malmazan/dev/chat_app/plugins/llm_plugin.py": "Current LLM implementation (to be migrated)"
    - "/Users/malmazan/dev/chat_app/core/llm/": "New core LLM system location"
    - "/Users/malmazan/dev/chat_app/core/models.py": "ConversationMessage data model"
  
  Key_Features_Delivered:
    - HTTP-based LLM API communication using aiohttp
    - Configurable API endpoint (default: localhost:1234)
    - Conversation message management with history limits
    - Status display integration showing processing state
    - Error handling for connection and timeout issues
    - Core system architecture (LLM as essential service)
    - Hook integration for pre/post LLM processing
    - Configurable parameters (temperature, timeout, max_history)
```

---

## US-2025-014
### Thinking Tag Processing

```yaml
Story_Metadata:
  Story_ID: "US-2025-014"
  Epic_ID: "EPIC-CORE-LLM-SYSTEM"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Done"
  Priority: "High"
  Size_Estimate: "5"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "6"
  Risk_Level: "Low"
  Dependencies_Identified: "US-2025-013 (Core LLM Communication)"
  Similar_Stories: "Chain-of-thought reasoning displays"
  Implementation_Suggestions: "Implemented in core LLM system with regex processing"
```

#### User Story Statement

**As a** end user  
**I want** to see the LLM's thinking process when it uses `<think>` tags  
**So that** I can understand the AI's reasoning and have transparency in its decision-making  

#### Story Context
Modern LLMs often benefit from showing their reasoning process. When LLMs use `<think>` tags to show their internal reasoning, users should be able to see this thinking process with appropriate visual treatment to distinguish it from the final response.

#### User Personas
```yaml
Primary_Persona:
  Name: "Dr. Sarah the AI Researcher"
  Role: "AI Researcher / Scientist"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Studies AI reasoning and decision-making processes
    - Values transparency in AI system behavior
    - Needs to analyze thinking patterns and reasoning chains
  Goals_and_Motivations:
    - Understanding AI reasoning processes
    - Transparency in AI decision-making
    - Ability to study thinking patterns
  Pain_Points:
    - Black-box AI responses without reasoning visibility
    - Difficulty analyzing AI decision-making process
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Display thinking content with visual effects"
      Given: "LLM response contains <think>reasoning content</think>"
      When: "Response is processed"
      Then: "Thinking content is displayed with shimmer effect"
      And: "Thinking text is visually distinct from final response"
      
    Scenario_2:
      Title: "Process multiple thinking sections"
      Given: "LLM response has multiple <think> sections"
      When: "Response is rendered"
      Then: "Each thinking section is displayed separately"
      And: "All thinking sections have consistent visual treatment"
      
    Scenario_3:
      Title: "Handle final response tags"
      Given: "LLM uses <final_response>content</final_response>"
      When: "Response is processed"
      Then: "Only final response content is shown in conversation"
      And: "Conversation flow terminates appropriately"
      
  Configuration_Scenarios:
    Scenario_4:
      Title: "Configure thinking display limits"
      Given: "Configuration sets thinking_message_limit: 2"
      When: "LLM generates extensive thinking"
      Then: "Only last 2 thinking messages are displayed"
      And: "Older thinking messages scroll off display"
```

#### Technical Implementation
```yaml
Implementation_Status:
  Files_To_Update:
    - "/Users/malmazan/dev/chat_app/core/llm/": "Thinking tag regex processing (migrated from plugin)"
    - "/Users/malmazan/dev/chat_app/core/io/layout.py": "Thinking animation display"
    - "/Users/malmazan/dev/chat_app/tests/test_core_llm.py": "Core LLM system test coverage"
  
  Key_Features_Delivered:
    - Regex-based extraction of <think> content
    - Visual effects for thinking text (shimmer/dim/normal)
    - Multiple thinking section support
    - Final response tag processing
    - Configurable thinking display limits
    - Thinking animation with vertical marquee effect
    - Time tracking for thinking duration display
    - Integration with visual effects system
```

---

# PLANNED USER STORIES

## US-2025-020
### Multi-Model Routing

```yaml
Story_Metadata:
  Story_ID: "US-2025-020"
  Epic_ID: "EPIC-ADVANCED-LLM"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "High"
  Size_Estimate: "13"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "8"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-013 (Basic LLM Communication)"
  Similar_Stories: "LangChain model routing, multi-agent systems"
  Implementation_Suggestions: "Extend LLM plugin with routing logic and model capabilities"
```

#### User Story Statement

**As a** power user  
**I want** the system to automatically route queries to appropriate models based on task requirements  
**So that** I get fast responses for simple queries and deep reasoning for complex problems  

#### Story Context
Different LLM models excel at different tasks. Fast models are better for quick responses and simple queries, while larger reasoning models are better for complex analysis. The system should intelligently route queries to the most appropriate model.

#### User Personas
```yaml
Primary_Persona:
  Name: "Taylor the Efficiency Expert"
  Role: "Technical Lead / Architect"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Values optimal performance and resource usage
    - Works with both simple and complex queries
    - Understands different model capabilities
  Goals_and_Motivations:
    - Fast responses for simple queries
    - Deep analysis for complex problems
    - Efficient resource utilization
  Pain_Points:
    - Slow responses for simple questions
    - Inadequate reasoning for complex problems
    - Manual model selection is inefficient
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Route simple queries to fast model"
      Given: "User asks a straightforward factual question"
      When: "Query is analyzed for complexity"
      Then: "Request is routed to fast model (e.g., qwen3-4b)"
      And: "Response is received within 2-3 seconds"
      
    Scenario_2:
      Title: "Route complex queries to reasoning model"
      Given: "User asks for detailed analysis or problem-solving"
      When: "Query complexity is determined to be high"
      Then: "Request is routed to reasoning model"
      And: "User sees indication of model selection"
      
    Scenario_3:
      Title: "Model collaboration workflow"
      Given: "Fast model provides initial response"
      When: "Fast model determines additional reasoning is needed"
      Then: "Query is escalated to reasoning model"
      And: "Both responses are presented coherently"
      
  Configuration_Scenarios:
    Scenario_4:
      Title: "Configure model routing rules"
      Given: "System administrator configures routing"
      When: "model_routing configuration is updated"
      Then: "New routing rules take effect"
      And: "Model selection reflects configuration changes"
```

#### Technical Implementation
```yaml
AI_Implementation_Suggestions:
  Recommended_Approach: "Extend LLM plugin with intelligent routing layer"
  
  Chat_App_Integration_Points:
    EventBus_Integration:
      - Events_To_Handle: ["USER_INPUT", "LLM_REQUEST"]
      - Events_To_Emit: ["MODEL_SELECTION", "MODEL_ROUTING"]
      - Hook_Priorities: ["500 for preprocessing, 100 for LLM selection"]
    
    Plugin_Considerations:
      - Plugin_Compatibility: "Extends existing LLM plugin"
      - New_Plugin_Opportunities: "Model selection advisor plugin"
      - Configuration_Changes: "Add model_routing section to config"
    
  Technical_Approach:
    Architecture_Pattern: "Strategy pattern for model selection"
    Key_Components:
      - "QueryAnalyzer: Determines query complexity and type"
      - "ModelRouter: Selects appropriate model based on analysis"
      - "CollaborationManager: Handles multi-model workflows"
    Data_Flow: "Query -> Analysis -> Model Selection -> API Call -> Response Processing"
    Error_Handling: "Fallback to default model on routing failure"
```

---

## US-2025-023
### MCP Server Integration

```yaml
Story_Metadata:
  Story_ID: "US-2025-023"
  Epic_ID: "EPIC-MCP-INTEGRATION"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "High"
  Size_Estimate: "21"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "9"
  Risk_Level: "High"
  Dependencies_Identified: "US-2025-005 (Hook System), MCP protocol understanding"
  Similar_Stories: "Language Server Protocol integration, plugin bridges"
  Implementation_Suggestions: "Create MCP bridge plugin with server discovery and communication"
```

#### User Story Statement

**As a** end user and system integrator  
**I want** LLMs to automatically discover, suggest, and invoke MCP-compatible tools during natural conversation  
**So that** I can access powerful external tools seamlessly through conversational AI without manual tool management  

#### Story Context
The Model Context Protocol (MCP) is an emerging standard for tool integration with LLM systems. True MCP integration means LLMs become **tool-aware** - they can automatically discover available MCP tools, suggest them contextually during conversation, and invoke them intelligently based on user needs. This transforms the chat experience from basic conversation to an AI assistant with access to a rich ecosystem of external capabilities.

#### User Personas
```yaml
Primary_Persona:
  Name: "Morgan the Integration Specialist"
  Role: "DevOps Engineer / System Integrator"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Manages multiple systems requiring integration
    - Prefers standard protocols over custom solutions
    - Values interoperability and existing tool reuse
  Goals_and_Motivations:
    - Leverage existing MCP-compatible tools
    - Standardized integration approach
    - Reduced custom development effort
  Pain_Points:
    - Custom integration for each tool is time-consuming
    - Lack of standardized tool integration protocols
    - Difficulty maintaining multiple custom integrations

Secondary_Persona:
  Name: "Alex the AI Power User"
  Role: "Developer / Researcher"
  Experience_Level: "Intermediate"
  Key_Characteristics:
    - Uses AI for complex workflows and automation
    - Expects AI to intelligently suggest and use tools
    - Values seamless tool integration in conversation
  Goals_and_Motivations:
    - AI assistant that knows what tools are available
    - Automatic tool suggestion based on conversation context
    - Seamless tool invocation without manual selection
  Pain_Points:
    - Having to manually discover and invoke tools
    - AI that doesn't know about available capabilities
    - Disconnected tool usage outside of conversation flow
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Discover available MCP servers"
      Given: "MCP servers are running on the system"
      When: "Application starts with MCP integration enabled"
      Then: "Available MCP servers are automatically discovered"
      And: "Server capabilities are cataloged and available"
      
    Scenario_2:
      Title: "Execute MCP tool through chat"
      Given: "MCP server with file operations is available"
      When: "User requests file listing through chat"
      Then: "Request is routed to appropriate MCP server"
      And: "MCP tool response is integrated into conversation"
      
    Scenario_3:
      Title: "MCP server health monitoring"
      Given: "MCP servers are integrated"
      When: "Server status is monitored"
      Then: "Server health is displayed in status areas"
      And: "Connection issues are reported clearly"
      
  LLM_MCP_Integration_Scenarios:
    Scenario_4:
      Title: "LLM discovers and suggests MCP tools"
      Given: "User asks 'Can you help me analyze this project structure?'"
      When: "LLM processes the query and checks available MCP tools"
      Then: "LLM responds 'I can help! I have access to file analysis tools. Let me scan your project structure.'"
      And: "LLM automatically suggests relevant MCP tools before invoking them"
      
    Scenario_5:
      Title: "LLM automatically invokes MCP tools based on context"
      Given: "User says 'Show me the recent Git commits'"
      When: "LLM analyzes the request and identifies MCP Git tools"
      Then: "LLM automatically calls the appropriate MCP Git server"
      And: "LLM integrates Git results into conversational response with analysis"
      
    Scenario_6:
      Title: "LLM maintains dynamic awareness of MCP capabilities"
      Given: "New MCP servers come online during conversation"
      When: "LLM plugin detects new MCP capabilities through discovery"
      Then: "LLM immediately updates its tool awareness"
      And: "LLM can suggest new tools in ongoing conversation without restart"
      
    Scenario_7:
      Title: "LLM explains MCP tool capabilities conversationally"
      Given: "User asks 'What can you help me with?'"
      When: "LLM analyzes available MCP tools"
      Then: "LLM responds with natural language description of available capabilities"
      And: "LLM explains how MCP tools extend its abilities with specific examples"
      
  Integration_Scenarios:
    Scenario_8:
      Title: "Bridge MCP tools to hook system"
      Given: "MCP server provides development tools"
      When: "Tools are registered with the system"
      Then: "MCP tools appear as available hooks"
      And: "LLM can discover and use MCP tools automatically"
      
  Error_and_Fallback_Scenarios:
    Scenario_9:
      Title: "Handle MCP tool failures gracefully in conversation"
      Given: "LLM attempts to invoke an MCP tool that fails"
      When: "MCP server returns error or times out"
      Then: "LLM acknowledges the failure conversationally"
      And: "LLM suggests alternative approaches or manual steps"
      
    Scenario_10:
      Title: "LLM adapts when MCP tools become unavailable"
      Given: "Previously available MCP tools go offline"
      When: "LLM attempts to use unavailable tools"
      Then: "LLM detects tool unavailability and informs user"
      And: "LLM adjusts its capability descriptions in future conversations"
```

#### Technical Implementation
```yaml
AI_Implementation_Suggestions:
  Recommended_Approach: "Create MCP bridge plugin with standardized server communication"
  
  Chat_App_Integration_Points:
    EventBus_Integration:
      - Events_To_Handle: ["TOOL_CALL", "MCP_REQUEST"]
      - Events_To_Emit: ["MCP_DISCOVERY", "MCP_RESPONSE", "MCP_ERROR"]
      - Hook_Priorities: ["800 for MCP tool routing"]
    
    Plugin_Considerations:
      - Plugin_Compatibility: "New MCP bridge plugin"
      - New_Plugin_Opportunities: "Individual MCP server adapter plugins"
      - Configuration_Changes: "Add mcp_integration section with server discovery"
    
  Technical_Approach:
    Architecture_Pattern: "Adapter pattern for MCP protocol bridge"
    Key_Components:
      - "MCPDiscovery: Finds and catalogs available MCP servers"
      - "MCPBridge: Translates between chat system and MCP protocol"
      - "ServerManager: Manages MCP server connections and health"
      - "LLMToolRegistry: Maintains LLM awareness of available MCP capabilities"
      - "ConversationalMCPInterface: Enables natural language MCP tool interaction"
      - "ToolSuggestionEngine: Provides context-aware tool recommendations"
    Data_Flow: "User Query -> LLM Analysis -> Tool Selection -> MCP Translation -> Server Communication -> Response Integration -> Conversational Presentation"
    LLM_Integration: "LLM plugin receives real-time updates of MCP capabilities and can invoke tools through natural language processing"
    Error_Handling: "MCP server failures, protocol errors, timeout handling, conversational error explanation"    
    
  Chat_App_LLM_Integration:
    LLM_Plugin_Enhancements:
      - "Dynamic MCP tool awareness during conversation"
      - "Context-aware tool suggestion based on user queries"
      - "Automatic tool invocation with conversational explanation"
      - "Real-time capability updates when MCP tools change"
    
    Hook_Integration_Points:
      - "PRE_LLM_REQUEST: Check available MCP tools for query"
      - "POST_LLM_RESPONSE: Parse LLM tool requests and invoke MCP"
      - "MCP_TOOL_RESULT: Integrate tool results back into LLM context"
      - "MCP_DISCOVERY_CHANGE: Update LLM tool awareness dynamically"
```

---

## US-2025-026
### Advanced Status Areas

```yaml
Story_Metadata:
  Story_ID: "US-2025-026"
  Epic_ID: "EPIC-ADVANCED-TERMINAL"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Medium"
  Size_Estimate: "8"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "6"
  Risk_Level: "Low"
  Dependencies_Identified: "US-2025-012 (Plugin Status Display)"
  Similar_Stories: "IDE status bars, terminal multiplexers"
  Implementation_Suggestions: "Enhance status_renderer.py with advanced layout options"
```

#### User Story Statement

**As a** power user  
**I want** configurable status areas (A, B, C) with flexible layout and plugin assignment  
**So that** I can customize my information display to match my workflow and priorities  

#### Story Context
The requirements document describes three status areas (A, B, C) with configurable positioning and content. This provides users with flexible information display that can be customized based on their specific needs and workflow preferences.

#### User Personas
```yaml
Primary_Persona:
  Name: "Quinn the Dashboard Enthusiast"
  Role: "Technical Project Manager"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Manages multiple projects simultaneously
    - Values comprehensive system visibility
    - Prefers customizable information displays
  Goals_and_Motivations:
    - Monitor multiple system aspects simultaneously
    - Customize information layout for efficiency
    - Quick visual assessment of system health
  Pain_Points:
    - Fixed status layouts don't match workflow
    - Too much or too little status information
    - Inability to prioritize status information
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Configure status area layout"
      Given: "User configures status areas in settings"
      When: "display.status_areas configuration is updated"
      Then: "Status areas A, B, C appear with specified layout"
      And: "Area A shows 50% width left, B shows 50% width right, C full width"
      
    Scenario_2:
      Title: "Assign plugins to status areas"
      Given: "Multiple plugins are available"
      When: "Plugin display.status_area is configured"
      Then: "Plugin status appears in assigned area"
      And: "Multiple plugins can share an area"
      
    Scenario_3:
      Title: "Dynamic status area content"
      Given: "Plugins are generating status information"
      When: "Status content changes"
      Then: "Status areas update in real-time"
      And: "Layout remains stable during updates"
      
  Customization_Scenarios:
    Scenario_4:
      Title: "Hide unused status areas"
      Given: "Status area has no active plugins"
      When: "Area would be empty"
      Then: "Empty status area is not displayed"
      And: "Layout adjusts to use available space"
```

#### Technical Implementation
```yaml
AI_Implementation_Suggestions:
  Recommended_Approach: "Enhance existing status system with layout configuration"
  
  Chat_App_Integration_Points:
    Terminal_Interface_Impact:
      - Display_Changes: "Configurable status area positioning and sizing"
      - User_Interaction: "Configuration-driven layout changes"
      - Visual_Feedback: "Clear visual separation between status areas"
    
  Technical_Approach:
    Architecture_Pattern: "Template pattern for status area layout"
    Key_Components:
      - "StatusLayoutManager: Handles area positioning and sizing"
      - "StatusContentRouter: Assigns plugin content to areas"
      - "StatusAreaRenderer: Renders individual status areas"
    Data_Flow: "Plugin Status -> Content Router -> Layout Manager -> Renderer"
    Error_Handling: "Handle plugin failures, layout constraint violations"
```

---

## US-2025-029
### Plugin Development SDK

```yaml
Story_Metadata:
  Story_ID: "US-2025-029"
  Epic_ID: "EPIC-DEVELOPER-EXPERIENCE"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "High"
  Size_Estimate: "13"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "7"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-009 (Plugin Loading), US-2025-005 (Hook System)"
  Similar_Stories: "VSCode extension SDK, WordPress plugin framework"
  Implementation_Suggestions: "Create plugin development toolkit with templates and validation"
```

#### User Story Statement

**As a** plugin developer  
**I want** comprehensive SDK with templates, documentation, and development tools  
**So that** I can quickly create high-quality plugins without understanding internal system details  

#### Story Context
To build a thriving plugin ecosystem, developers need excellent tooling. This includes plugin templates, development utilities, validation tools, and comprehensive documentation that makes plugin development accessible to developers of all skill levels.

#### User Personas
```yaml
Primary_Persona:
  Name: "Alex the Plugin Developer"
  Role: "Independent Developer"
  Experience_Level: "Intermediate"
  Key_Characteristics:
    - Creates plugins for personal and community use
    - Values clear documentation and examples
    - Needs reliable development and testing tools
  Goals_and_Motivations:
    - Quick plugin development and deployment
    - Understanding of plugin best practices
    - Reliable testing and validation tools
  Pain_Points:
    - Steep learning curve for new plugin systems
    - Lack of development tooling and examples
    - Difficulty debugging plugin integration issues
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Generate plugin from template"
      Given: "Developer runs plugin generation command"
      When: "Template parameters are provided"
      Then: "Complete plugin structure is generated"
      And: "Plugin includes boilerplate code and documentation"
      
    Scenario_2:
      Title: "Validate plugin before deployment"
      Given: "Developer completes plugin implementation"
      When: "Plugin validation tool is run"
      Then: "Plugin is checked for common issues"
      And: "Validation report with recommendations is provided"
      
    Scenario_3:
      Title: "Test plugin in isolation"
      Given: "Plugin is in development"
      When: "Developer runs plugin test suite"
      Then: "Plugin can be tested without full system"
      And: "Mock system components are available for testing"
      
  Developer_Experience_Scenarios:
    Scenario_4:
      Title: "Plugin development documentation"
      Given: "Developer is creating their first plugin"
      When: "Developer accesses plugin SDK documentation"
      Then: "Comprehensive guides and examples are available"
      And: "API reference is complete and accurate"
```

---

## US-2025-032
### Advanced Monitoring

```yaml
Story_Metadata:
  Story_ID: "US-2025-032"
  Epic_ID: "EPIC-SYSTEM-ADMINISTRATION"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Medium"
  Size_Estimate: "13"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "7"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-008 (Hook Status Monitoring)"
  Similar_Stories: "Application Performance Monitoring, system observability"
  Implementation_Suggestions: "Create monitoring plugin with metrics collection and alerting"
```

#### User Story Statement

**As a** system administrator  
**I want** comprehensive monitoring of system performance, plugin health, and resource usage  
**So that** I can ensure system reliability and optimize performance proactively  

#### Story Context
Production systems require comprehensive monitoring to ensure reliability and performance. This includes tracking plugin performance, resource usage, error rates, and system health metrics with alerting capabilities.

#### User Personas
```yaml
Primary_Persona:
  Name: "Casey the SRE"
  Role: "Site Reliability Engineer"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Responsible for system uptime and performance
    - Values proactive monitoring and alerting
    - Needs detailed metrics for troubleshooting
  Goals_and_Motivations:
    - Prevent system issues before they impact users
    - Quick identification of performance bottlenecks
    - Comprehensive system health visibility
  Pain_Points:
    - Lack of visibility into system internals
    - Reactive rather than proactive issue resolution
    - Insufficient metrics for performance optimization
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Primary_Scenarios:
    Scenario_1:
      Title: "Monitor plugin performance"
      Given: "Monitoring system is enabled"
      When: "Plugins are executing hooks"
      Then: "Hook execution times are tracked"
      And: "Performance metrics are collected and stored"
      
    Scenario_2:
      Title: "Track resource usage"
      Given: "System is running with multiple plugins"
      When: "Resource monitoring is active"
      Then: "CPU, memory, and I/O usage is tracked per plugin"
      And: "Resource usage trends are analyzed"
      
    Scenario_3:
      Title: "Alert on performance issues"
      Given: "Performance thresholds are configured"
      When: "Hook execution exceeds timeout"
      Then: "Alert is generated with context"
      And: "Alert includes suggested remediation steps"
      
  System_Health_Scenarios:
    Scenario_4:
      Title: "System health dashboard"
      Given: "Monitoring data is being collected"
      When: "Administrator accesses health dashboard"
      Then: "Comprehensive system status is displayed"
      And: "Historical trends and patterns are visible"
```

---

## Story Dependencies and Epic Relationships

### Implementation Priority Matrix

```yaml
Epic_Priorities:
  EPIC-TERMINAL-INTERFACE:
    Status: "Completed"
    Stories: ["US-2025-001", "US-2025-002", "US-2025-003", "US-2025-004"]
    Business_Value: "Foundation for all functionality"
    
  EPIC-HOOK-SYSTEM:
    Status: "Completed"  
    Stories: ["US-2025-005", "US-2025-006", "US-2025-007", "US-2025-008"]
    Business_Value: "Core extensibility architecture"
    
  EPIC-PLUGIN-SYSTEM:
    Status: "Completed"
    Stories: ["US-2025-009", "US-2025-010", "US-2025-011", "US-2025-012"]
    Business_Value: "Enables third-party extensions"
    
  EPIC-CORE-LLM-SYSTEM:
    Status: "Basic Complete, Migration Needed"
    Stories: ["US-2025-013", "US-2025-014", "US-2025-015", "US-2025-016", "US-2025-045"]
    Business_Value: "Essential AI functionality as core service"
    Architecture_Change: "Migrated from plugin to core system (core/llm/)"
    Next_Phase: ["US-2025-020", "US-2025-021", "US-2025-022"]
    
  EPIC-ADVANCED-LLM:
    Status: "Planned"
    Stories: ["US-2025-020", "US-2025-021", "US-2025-022"]
    Business_Value: "Advanced AI capabilities"
    Dependencies: ["EPIC-CORE-LLM-SYSTEM"]
    
  EPIC-MCP-INTEGRATION:
    Status: "Planned"
    Stories: ["US-2025-023", "US-2025-024", "US-2025-025"]
    Business_Value: "Ecosystem integration"
    Dependencies: ["EPIC-HOOK-SYSTEM"]
    
  EPIC-DEVELOPER-EXPERIENCE:
    Status: "Planned"
    Stories: ["US-2025-029", "US-2025-030", "US-2025-031"]
    Business_Value: "Plugin ecosystem growth"
    Dependencies: ["EPIC-PLUGIN-SYSTEM"]
    
  EPIC-SYSTEM-ADMINISTRATION:
    Status: "Planned"
    Stories: ["US-2025-032", "US-2025-033", "US-2025-034"]
    Business_Value: "Production readiness"
    Dependencies: ["EPIC-HOOK-SYSTEM", "EPIC-PLUGIN-SYSTEM"]
```

### Critical Path Analysis

```yaml
Phase_1_Foundation: "✅ COMPLETED"
  - Core terminal interface with real-time rendering
  - Universal hook system with event bus
  - Plugin discovery and lifecycle management
  - Basic LLM communication with thinking tags
  - Configuration and state management
  
Phase_2_Enhancement: "📋 NEXT PRIORITY"
  Priority_Order:
    1. "US-2025-020: Multi-Model Routing" # Immediate user value
    2. "US-2025-023: MCP Server Integration" # Ecosystem expansion
    3. "US-2025-026: Advanced Status Areas" # User experience
    4. "US-2025-029: Plugin Development SDK" # Developer enablement
    
Phase_3_Production: "🔮 FUTURE"
  - Advanced monitoring and observability
  - Performance optimization and scaling
  - Security sandbox and plugin isolation
  - Production deployment tools
```

---

## Success Metrics and Validation

### Story Success Framework

```yaml
Implementation_Success_Metrics:
  Technical_Metrics:
    - "Test Coverage: >90% for all implemented stories"
    - "Performance: Maintain 20 FPS rendering with <5% CPU"
    - "Memory Usage: <100MB baseline, <10MB per plugin"
    - "Startup Time: <2 seconds to operational state"
    
  User_Experience_Metrics:
    - "Response Time: <3 seconds for simple queries"
    - "Visual Feedback: Status updates within 100ms"
    - "Error Recovery: Clear error messages with recovery guidance"
    - "Plugin Integration: New plugins load within 5 seconds"
    
  Ecosystem_Success_Metrics:
    - "Plugin Adoption: >10 community plugins within 6 months"
    - "Developer Satisfaction: >8/10 in plugin developer surveys"
    - "Documentation Quality: <5 minutes to create basic plugin"
    - "Integration Success: >95% of attempted integrations succeed"
    
Business_Value_Validation:
  Foundation_Stories: "Validated through successful implementation and testing"
  Advanced_Features: "Will be validated through user feedback and adoption metrics"
  Ecosystem_Growth: "Measured by plugin development activity and community engagement"
```

---

## Conclusion

This comprehensive user story collection represents both the current foundational implementation and the ambitious vision outlined in the requirements document. The Chat App has successfully established a robust foundation with:

- **Universal Hook System**: Every system action can be intercepted and customized
- **Plugin Architecture**: Dynamic discovery and loading with comprehensive lifecycle management  
- **Real-time Terminal Interface**: Smooth 20 FPS rendering with visual effects
- **LLM Integration**: Complete communication system with thinking tag processing
- **Configuration Management**: Flexible, persistent configuration with plugin integration

The planned features represent the next evolution toward a truly comprehensive, customizable LLM terminal interface that leverages MCP integration, multi-model routing, and advanced developer tools to create an unparalleled platform for AI-assisted terminal interaction.

Each story includes detailed acceptance criteria, implementation suggestions, and clear validation approaches to ensure successful delivery while maintaining the system's core philosophy: "everything has a hook."

# CRITICAL FOUNDATION USER STORIES

## US-2025-035
### System Stability and Performance Optimization

```yaml
Story_Metadata:
  Story_ID: "US-2025-035"
  Epic_ID: "EPIC-SYSTEM-STABILITY"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Critical"
  Size_Estimate: "21"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "9"
  Risk_Level: "High"
  Dependencies_Identified: "All existing core systems"
  Similar_Stories: "System reliability, performance optimization"
  Implementation_Suggestions: "Comprehensive stability framework with monitoring and recovery"
```

#### User Story Statement

**As a** system administrator and end user  
**I want** the Kollabor CLI interface to be rock-solid stable and performant under all conditions  
**So that** the system never crashes, becomes unresponsive, or degrades regardless of plugin behavior or system load  

#### Story Context
The Chat App must be bulletproof - users depend on it for critical workflows, and Marco's family relies on these systems working correctly. **Current critical issues**: Core LLM system tool calls sometimes display blank, input box artifacts appear in conversation flow, clipboard paste only shows 2 characters initially requiring spacebar to complete, and rendering inconsistencies at 20 FPS make the system "very finicky." These stability issues must be resolved before adding new features.

**Architectural Change**: LLM functionality has been moved from plugin to core system (`core/llm/`) as it's essential functionality that other components depend on.

#### User Personas
```yaml
Primary_Persona:
  Name: "Marco the System Owner"
  Role: "Project Owner / Power User"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Systems must work reliably for family's livelihood
    - Cannot tolerate system crashes or degradation
    - Needs predictable, consistent performance
  Goals_and_Motivations:
    - 99.9% system uptime and reliability
    - Protection from plugin failures
    - Consistent performance under any load
  Pain_Points:
    - Core LLM system tool calls showing blank instead of content
    - Input box artifacts appearing in conversation flow
    - Clipboard paste issues (partial paste, spacebar completion)
    - Inconsistent 20 FPS rendering performance
    - System "finicky" behavior affecting daily use
    - Need for LLM as core service rather than optional plugin

Secondary_Personas:
  - Name: "Plugin Developer"
    Role: "Third-party Developer"
    Relevance: "Plugins must not be able to crash the system"
  - Name: "Production User"
    Role: "Daily User"
    Relevance: "Needs reliable tool for critical work"
```

#### AI-Enhanced Story Analysis

```yaml
AI_Complexity_Analysis:
  Technical_Complexity: "9 - System-wide stability requires deep architectural changes"
  Business_Logic_Complexity: "7 - Complex error handling and recovery scenarios"
  UI/UX_Complexity: "6 - Must maintain smooth experience during failures"
  Integration_Complexity: "9 - All plugins and components must be isolation-safe"
  Testing_Complexity: "9 - Requires comprehensive failure scenario testing"
  
Overall_Complexity_Score: "8.5 - Critical foundational work affecting entire system"

Complexity_Factors:
  High_Complexity_Indicators:
    - "Plugin isolation requires sandboxing architecture"
    - "Memory leak detection and prevention across all components"
    - "Performance monitoring and adaptive behavior implementation"
    - "Graceful degradation under extreme load conditions"
  
  Simplifying_Factors:
    - "Existing async architecture provides good foundation"
    - "Current modular design enables targeted improvements"
  
Risk_Factors:
  Technical_Risks:
    - "Plugin sandboxing may impact performance significantly"
    - "Memory management changes could introduce new bugs"
  
  Business_Risks:
    - "System instability affects user trust and adoption"
    - "Poor performance drives users to alternatives"
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Core_Stability_Scenarios:
    Scenario_1:
      Title: "Core LLM tool calls display correctly"
      Given: "Core LLM system executes tool calls"
      When: "Tool call results are processed"
      Then: "Tool call content appears correctly in terminal"
      And: "No blank or missing content is displayed"
      And: "Tool call output flows naturally in conversation"
      
    Scenario_1b:
      Title: "Plugin cannot crash core system"
      Given: "Plugin contains infinite loop or memory leak"
      When: "Plugin is executed and begins consuming resources"
      Then: "Core system detects resource abuse within 5 seconds"
      And: "Plugin is automatically isolated/terminated"
      And: "Core system continues operating normally"
      
    Scenario_2:
      Title: "Clean terminal flow without artifacts"
      Given: "User input is processed and displayed"
      When: "Content flows from input box to conversation area"
      Then: "No input box artifacts appear in conversation"
      And: "Terminal flow appears natural and clean"
      And: "Input buffer correctly clears after submission"
      
    Scenario_2b:
      Title: "System recovers from plugin failures"
      Given: "Plugin throws unhandled exception"
      When: "Plugin error occurs during hook execution"
      Then: "Error is contained to plugin scope"
      And: "Other plugins and core system continue functioning"
      And: "User receives clear error notification with recovery options"
      
    Scenario_3:
      Title: "Clipboard paste works correctly"
      Given: "User has content in system clipboard"
      When: "User pastes content into input box"
      Then: "All clipboard content appears immediately"
      And: "No spacebar required to complete paste operation"
      And: "Paste operation completes in single render cycle"
      
    Scenario_3b:
      Title: "Memory usage remains bounded"
      Given: "System runs for extended periods with multiple plugins"
      When: "Memory monitoring is active"
      Then: "Total memory usage stays below 500MB baseline"
      And: "No memory leaks detected over 24-hour period"
      And: "Automatic garbage collection prevents accumulation"
      
  Performance_Scenarios:
    Scenario_4:
      Title: "Maintain consistent 20 FPS rendering"
      Given: "System is running with standard plugin load"
      When: "Rendering loop is active"
      Then: "Achieves stable 20 FPS rendering rate"
      And: "Frame rate is displayed in debug status area"
      And: "No rendering inconsistencies or stuttering occurs"
      And: "User interaction remains responsive at all times"
      
    Scenario_5:
      Title: "Adaptive performance under idle conditions"
      Given: "No user activity for 30 seconds"
      When: "System enters idle mode"
      Then: "Rendering drops to 2 FPS to conserve resources"
      And: "Plugin updates are throttled appropriately"
      And: "Returns to full performance on user interaction"
      
  Debug_and_Troubleshooting_Scenarios:
    Scenario_6:
      Title: "Debug information available for troubleshooting"
      Given: "System is running and user needs troubleshooting info"
      When: "Debug mode is enabled"
      Then: "Current frame rate is displayed in status area"
      And: "Rendering performance metrics are visible"
      And: "Plugin execution timing is shown when needed"
      
  Error_Recovery_Scenarios:
    Scenario_7:
      Title: "Graceful degradation on resource exhaustion"
      Given: "System approaches resource limits"
      When: "CPU or memory usage exceeds 90%"
      Then: "Non-essential features are automatically disabled"
      And: "Core chat functionality remains available"
      And: "User is notified of degraded mode with recovery steps"
      
    Scenario_8:
      Title: "Automatic recovery from transient failures"
      Given: "Network or LLM API becomes temporarily unavailable"
      When: "Service failure is detected"
      Then: "System enters offline mode gracefully"
      And: "Automatic retry with exponential backoff is initiated"
      And: "Full functionality resumes when service returns"
```

#### Technical Implementation
```yaml
AI_Implementation_Suggestions:
  Recommended_Approach: "Multi-layered stability framework with monitoring and recovery"
  
  Chat_App_Integration_Points:
    Core_System_Changes:
      - "Core LLM system integration and stability"
      - "Plugin sandboxing with resource limits and timeouts"
      - "Memory monitoring and automatic garbage collection"
      - "Adaptive rendering based on system load and activity"
      - "Circuit breakers for external service dependencies"
    
    EventBus_Integration:
      - Events_To_Handle: ["PLUGIN_ERROR", "RESOURCE_WARNING", "PERFORMANCE_DEGRADATION"]
      - Events_To_Emit: ["SYSTEM_RECOVERY", "PERFORMANCE_MODE_CHANGE", "STABILITY_ALERT"]
      - Hook_Priorities: ["999 for stability monitoring hooks"]
    
    Plugin_Considerations:
      - Plugin_Compatibility: "All plugins must comply with resource limits"
      - New_Plugin_Opportunities: "System monitoring and health check plugins"
      - Configuration_Changes: "Add stability and performance configuration section"
    
  Technical_Approach:
    Architecture_Pattern: "Circuit breaker and bulkhead patterns for fault isolation"
    Key_Components:
      - "ResourceMonitor: Tracks CPU, memory, and I/O usage per plugin"
      - "PluginSandbox: Enforces resource limits and timeouts"
      - "PerformanceManager: Adaptive rendering and throttling"
      - "RecoveryManager: Handles failures and system restoration"
      - "HealthChecker: Continuous system health monitoring"
    Data_Flow: "Continuous Monitoring -> Threat Detection -> Isolation/Recovery -> Performance Adaptation"
    Error_Handling: "Graceful degradation, automatic recovery, user notification"
    
  Specific_Implementation_Areas:
    Critical_Bug_Fixes_Priority_1:
      - "Fix core LLM system tool call display issues - ensure content appears correctly"
      - "Eliminate input box artifacts appearing in conversation flow"
      - "Fix clipboard paste to show full content immediately (no spacebar required)"
      - "Stabilize 20 FPS rendering consistency without stuttering"
      - "Add debug frame rate display in status area for troubleshooting"
      - "Migrate LLM functionality from plugin to core system (core/llm/)"
    
    Terminal_Flow_Stability:
      - "Clean input buffer management without artifacts"
      - "Proper content flow from input to conversation area"
      - "Reliable clipboard integration with full immediate paste"
      - "Consistent terminal rendering pipeline"
      - "Buffer manager improvements (reference: core/io/buffer_manager.py)"
    
    Debug_and_Monitoring:
      - "Real-time frame rate display in status area for troubleshooting"
      - "Plugin execution timing visibility"
      - "Memory usage tracking per component"
      - "Rendering performance metrics and diagnostics"
      - "Core LLM system tool call debugging information"
    
    Plugin_Isolation:
      - "Resource quotas per plugin (memory, CPU, I/O)"
      - "Execution timeouts with automatic termination"
      - "Separate process/thread pools for plugin execution"
    
    Performance_Optimization:
      - "Stable 20 FPS maintenance under all conditions"
      - "Dirty region tracking for efficient rendering"
      - "Background task coordination to prevent interference"
      - "Clipboard paste performance optimization"
    
    Recovery_Systems:
      - "Automatic plugin restart on failure"
      - "Configuration rollback on startup failures"
      - "Emergency safe mode with minimal functionality"
```

---

## US-2025-038
### Multi-row Terminal Navigation System

```yaml
Story_Metadata:
  Story_ID: "US-2025-038"
  Epic_ID: "EPIC-INTERACTIVE-TERMINAL"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Critical"
  Size_Estimate: "13"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "8"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-035 (System Stability)"
  Similar_Stories: "IDE navigation, terminal multiplexers"
  Implementation_Suggestions: "Multi-row terminal interface with cursor navigation"
```

#### User Story Statement

**As a** power user  
**I want** to navigate up and down through multiple rows in the terminal interface using arrow keys  
**So that** I can interact with slash commands, select options, and manage complex terminal layouts  

#### Story Context
The terminal interface needs to support interactive navigation beyond just the input line. When slash commands show options or when multiple interactive elements are present, users need to navigate up/down with arrow keys and make selections with Enter. This is foundational for the slash command system and interactive features.

#### User Personas
```yaml
Primary_Persona:
  Name: "Marco the Power User"
  Role: "System Owner / Daily User"
  Experience_Level: "Advanced"
  Key_Characteristics:
    - Expects vim-like navigation efficiency
    - Uses keyboard shortcuts extensively
    - Needs quick interaction with system options
  Goals_and_Motivations:
    - Fast navigation through interface options
    - Keyboard-driven interaction without mouse
    - Efficient system configuration and control
  Pain_Points:
    - Single-line input limiting interaction complexity
    - No way to select from multiple options
    - Limited interface real estate for complex features
```

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Navigation_Scenarios:
    Scenario_1:
      Title: "Up/down arrow navigation"
      Given: "Multiple interactive rows are displayed"
      When: "User presses up or down arrow keys"
      Then: "Cursor moves to previous/next selectable row"
      And: "Current selection is visually highlighted"
      And: "Navigation wraps around at boundaries"
      
    Scenario_2:
      Title: "Enter key selection"
      Given: "User has navigated to a selectable option"
      When: "User presses Enter key"
      Then: "Selected option is activated/executed"
      And: "Interface responds appropriately to selection"
      
    Scenario_3:
      Title: "Escape key to return to input"
      Given: "User is navigating interactive elements"
      When: "User presses Escape key"
      Then: "Navigation mode exits"
      And: "Cursor returns to main input area"
      
  Multi_Row_Display_Scenarios:
    Scenario_4:
      Title: "Dynamic row allocation"
      Given: "Interface needs to display interactive options"
      When: "Multi-row mode is activated"
      Then: "Additional terminal rows are allocated"
      And: "Content is displayed in structured layout"
      And: "Input area adjusts position accordingly"
```

#### Technical Implementation
```yaml
AI_Implementation_Suggestions:
  Recommended_Approach: "Extend terminal renderer with multi-row navigation state"
  
  Chat_App_Integration_Points:
    Terminal_Interface_Changes:
      - "Multi-row cursor position tracking"
      - "Visual highlighting for current selection"
      - "Dynamic terminal layout adjustment"
      - "Navigation state management"
    
    Key_Components:
      - "NavigationManager: Handles cursor movement and selection"
      - "RowManager: Manages dynamic row allocation"
      - "SelectionRenderer: Visual feedback for current selection"
      - "InteractionHandler: Processes navigation key events"
```

---

## US-2025-039
### Interactive Selection and Input Management

```yaml
Story_Metadata:
  Story_ID: "US-2025-039"
  Epic_ID: "EPIC-INTERACTIVE-TERMINAL"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "High"
  Size_Estimate: "8"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "7"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-038 (Multi-row Navigation)"
  Similar_Stories: "CLI selection menus, interactive prompts"
  Implementation_Suggestions: "Selection state management with input handling"
```

#### User Story Statement

**As a** user  
**I want** to select from multiple options and provide input in interactive contexts  
**So that** I can configure plugins, execute commands, and interact with complex interfaces efficiently  

#### Story Context
Building on the navigation system, users need the ability to make selections and provide additional input when interacting with slash commands and plugin configurations. This includes selecting from lists, entering values, and confirming actions.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Selection_Scenarios:
    Scenario_1:
      Title: "List selection interface"
      Given: "Multiple options are presented in a list"
      When: "User navigates and selects an option"
      Then: "Selected option is clearly highlighted"
      And: "Selection state is maintained across navigation"
      
    Scenario_2:
      Title: "Input prompts during selection"
      Given: "Selected option requires additional input"
      When: "User activates the option"
      Then: "Input prompt appears inline"
      And: "User can type additional information"
      And: "Tab/Enter moves to next field or confirms"
```

---

## US-2025-041
### Conversation History and Prompt Storage

```yaml
Story_Metadata:
  Story_ID: "US-2025-041"
  Epic_ID: "EPIC-DATA-PERSISTENCE"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "High"
  Size_Estimate: "8"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "6"
  Risk_Level: "Low"
  Dependencies_Identified: "US-2025-035 (System Stability)"
  Similar_Stories: "Chat history, command history"
  Implementation_Suggestions: "SQLite-based conversation and prompt storage"
```

#### User Story Statement

**As a** user  
**I want** all my input prompts and conversations to be stored persistently  
**So that** I can review previous interactions, search conversation history, and maintain context across sessions  

#### Story Context
Every prompt entered in the input bar should be stored for future reference. Users need to access conversation history, search previous interactions, and maintain context across application restarts. This is critical for workflow continuity and reference purposes.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Storage_Scenarios:
    Scenario_1:
      Title: "All input prompts are stored"
      Given: "User types message in input bar"
      When: "User presses Enter to send message"
      Then: "Message is stored in conversation history"
      And: "Timestamp and metadata are recorded"
      And: "Storage persists across application restarts"
      
    Scenario_2:
      Title: "Conversation context is maintained"
      Given: "User has ongoing conversation with LLM"
      When: "Application is restarted"
      Then: "Previous conversation context is restored"
      And: "LLM can reference previous conversation"
      
    Scenario_3:
      Title: "History search capability"
      Given: "User has extensive conversation history"
      When: "User searches for specific content"
      Then: "Relevant conversations are found and displayed"
      And: "Search supports keywords and date ranges"
```

---

## US-2025-042
### Slash Commands Core System

```yaml
Story_Metadata:
  Story_ID: "US-2025-042"
  Epic_ID: "EPIC-SLASH-COMMANDS"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Critical"
  Size_Estimate: "21"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "9"
  Risk_Level: "High"
  Dependencies_Identified: "US-2025-038 (Multi-row Navigation), US-2025-039 (Interactive Selection)"
  Similar_Stories: "Discord slash commands, CLI command systems"
  Implementation_Suggestions: "Plugin-extensible slash command framework"
```

#### User Story Statement

**As a** user and plugin developer  
**I want** a comprehensive slash command system that plugins can extend  
**So that** I can configure the system, manage plugins, and access functionality through intuitive commands like `/llm provider` or `/plugin disable`  

#### Story Context
Slash commands are CORE functionality that enable real-time system configuration and plugin management. The system must support `/llm provider`, `/plugin enable/disable`, `/help`, and allow plugins to register their own commands. Commands should trigger interactive interfaces with up/down navigation and selection.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Core_Command_Scenarios:
    Scenario_1:
      Title: "Basic slash command recognition"
      Given: "User types '/help' in input bar"
      When: "User presses Enter"
      Then: "Available commands are displayed in interactive list"
      And: "User can navigate with arrow keys"
      And: "Commands are categorized by plugin"
      
    Scenario_2:
      Title: "Core LLM configuration via slash commands"
      Given: "User types '/llm provider'"
      When: "Command is executed"
      Then: "Available LLM providers are shown in selection interface"
      And: "Current provider is highlighted"
      And: "User can select new provider with Enter"
      And: "Core LLM system configuration updates in real-time without restart"
      
    Scenario_3:
      Title: "Plugin management commands"
      Given: "User types '/plugin llm_plugin disable'"
      When: "Command is executed"
      Then: "Plugin is disabled immediately"
      And: "System adjusts functionality without restart"
      And: "Status is reflected in plugin display areas"
      
  Plugin_Integration_Scenarios:
    Scenario_4:
      Title: "Plugins can register slash commands"
      Given: "Plugin wants to register '/myplugin config'"
      When: "Plugin initialization occurs"
      Then: "Command is registered in slash command system"
      And: "Command appears in /help listing"
      And: "Plugin receives command events when invoked"
```

#### Technical Implementation
```yaml
AI_Implementation_Suggestions:
  Recommended_Approach: "Plugin-extensible command registry with interactive UI"
  
  Chat_App_Integration_Points:
    EventBus_Integration:
      - Events_To_Handle: ["SLASH_COMMAND", "COMMAND_COMPLETION", "PLUGIN_REGISTER_COMMAND"]
      - Events_To_Emit: ["COMMAND_EXECUTED", "PLUGIN_STATE_CHANGE", "CONFIG_UPDATE"]
      - Hook_Priorities: ["900 for command processing"]
    
    Key_Components:
      - "SlashCommandRegistry: Central command registration and routing"
      - "CommandParser: Parse and validate slash command syntax"
      - "InteractiveCommandUI: Handle command interfaces and selection"
      - "PluginCommandBridge: Allow plugins to register commands"
      - "CommandExecutor: Execute commands and handle real-time config updates"
```

---

## US-2025-043
### Security and Command Restriction System

```yaml
Story_Metadata:
  Story_ID: "US-2025-043"
  Epic_ID: "EPIC-SECURITY"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Critical"
  Size_Estimate: "13"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "8"
  Risk_Level: "High"
  Dependencies_Identified: "US-2025-035 (System Stability)"
  Similar_Stories: "Sandboxing, command filtering, security policies"
  Implementation_Suggestions: "Command filtering and LLM output sanitization"
```

#### User Story Statement

**As a** system administrator  
**I want** to prevent LLMs from executing dangerous commands like file deletion or system modification  
**So that** the system remains secure even when LLMs attempt to run restricted operations in sandboxed environments  

#### Story Context
LLMs sometimes try to execute commands like `rm`, `sudo`, or other system modifications. The system needs robust security to prevent dangerous operations while allowing safe tool use. This includes command filtering, output sanitization, and restricted execution environments.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Command_Restriction_Scenarios:
    Scenario_1:
      Title: "Dangerous commands are blocked"
      Given: "LLM attempts to execute 'rm -rf /'"
      When: "Command is processed"
      Then: "Command is blocked before execution"
      And: "User receives security warning"
      And: "Alternative safe commands are suggested"
      
    Scenario_2:
      Title: "Configurable command whitelist"
      Given: "System has configurable allowed commands"
      When: "LLM attempts command execution"
      Then: "Only whitelisted commands are permitted"
      And: "Command parameters are validated"
      
    Scenario_3:
      Title: "Sandboxed execution environment"
      Given: "LLM executes approved commands"
      When: "Commands run in sandbox"
      Then: "Commands cannot access sensitive file system areas"
      And: "Network access is restricted as configured"
      And: "Resource usage is limited and monitored"
```

---

## US-2025-044
### Multi-line Input Expansion

```yaml
Story_Metadata:
  Story_ID: "US-2025-044"
  Epic_ID: "EPIC-INPUT-ENHANCEMENT"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Medium"
  Size_Estimate: "5"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "6"
  Risk_Level: "Low"
  Dependencies_Identified: "US-2025-038 (Multi-row Navigation)"
  Similar_Stories: "Text editors, multi-line input fields"
  Implementation_Suggestions: "Dynamic input area expansion based on content"
```

#### User Story Statement

**As a** user  
**I want** the input area to automatically expand to multiple lines when my query is long  
**So that** I can write complex prompts without horizontal scrolling or line wrapping issues  

#### Story Context
When users type long queries, the input area should dynamically expand downward, wrapping text to the next line based on terminal width. This provides a natural writing experience for complex prompts and multi-paragraph input.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Dynamic_Expansion_Scenarios:
    Scenario_1:
      Title: "Auto-expand based on terminal width"
      Given: "User types text longer than terminal width"
      When: "Text reaches terminal boundary"
      Then: "Input area expands to next line"
      And: "Text wraps naturally at word boundaries"
      
    Scenario_2:
      Title: "Multi-line editing support"
      Given: "Input spans multiple lines"
      When: "User edits text"
      Then: "Cursor moves correctly within multi-line content"
      And: "Line breaks are preserved appropriately"
```

---

## US-2025-045
### Enhanced Model Routing System

```yaml
Story_Metadata:
  Story_ID: "US-2025-045"
  Epic_ID: "EPIC-CORE-LLM-SYSTEM"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "High"
  Size_Estimate: "13"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "8"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-013 (Core LLM System), US-2025-020 (Multi-Model Routing)"
  Similar_Stories: "Model orchestration, intelligent routing"
  Implementation_Suggestions: "Core LLM system enhancement with configurable model types and routing logic"
```

#### User Story Statement

**As a** power user  
**I want** to configure different model types (fast, reasoning, coding, documentation) and assign specific models to each type  
**So that** I can optimize model selection for different tasks and set default properties per model type  

#### Story Context
Users need the ability to define model categories like fast_model, reasoning_model, coding_model, documentation_model, and assign specific models to these roles. Each model type should support default properties and routing logic. This enhancement to the core LLM system provides intelligent model selection based on query analysis.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Model_Configuration_Scenarios:
    Scenario_1:
      Title: "Configure model types and assignments"
      Given: "User configures model routing in settings"
      When: "Model types are defined (fast, reasoning, coding, docs)"
      Then: "Each type can be assigned a specific model"
      And: "Default properties can be set per model type"
      
    Scenario_2:
      Title: "Intelligent model routing based on query type"
      Given: "User submits a coding-related query"
      When: "Query is analyzed for type classification"
      Then: "Request is routed to designated coding model"
      And: "Model-specific properties are applied"
```

---

## US-2025-046
### Plugin Management via Slash Commands

```yaml
Story_Metadata:
  Story_ID: "US-2025-046"
  Epic_ID: "EPIC-SLASH-COMMANDS"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Medium"
  Size_Estimate: "8"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "7"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-042 (Slash Commands Core)"
  Similar_Stories: "Package managers, plugin management interfaces"
  Implementation_Suggestions: "Plugin control via command interface"
```

#### User Story Statement

**As a** system administrator  
**I want** to enable, disable, and configure plugins using slash commands  
**So that** I can manage plugin state dynamically without system restarts using commands like `/plugin llm_plugin disable`  

#### Story Context
Plugin management should be seamlessly integrated into the slash command system. Users need commands to enable/disable plugins, view plugin status, and configure plugin settings in real-time.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Plugin_Control_Scenarios:
    Scenario_1:
      Title: "Enable/disable plugins via commands"
      Given: "User types '/plugin llm_plugin disable'"
      When: "Command is executed"
      Then: "Plugin is disabled without system restart"
      And: "Plugin status updates in real-time"
      
    Scenario_2:
      Title: "Plugin status and configuration display"
      Given: "User types '/plugin llm_plugin'"
      When: "Command shows plugin options"
      Then: "Available sub-commands are displayed (enable, disable, config)"
      And: "Current plugin status is shown"
```

---

## US-2025-047
### Command-line Integration System

```yaml
Story_Metadata:
  Story_ID: "US-2025-047"
  Epic_ID: "EPIC-COMMAND-INTEGRATION"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Medium"
  Size_Estimate: "8"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "7"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-041 (Conversation History)"
  Similar_Stories: "Terminal command integration, shell interaction"
  Implementation_Suggestions: "Command execution with conversation integration"
```

#### User Story Statement

**As a** developer  
**I want** to execute terminal commands using `$` prefix and have them included in conversation history  
**So that** the LLM can see what commands I've run and provide context-aware assistance  

#### Story Context
When the input box is empty and user types `$ls`, the command should be executed and added to conversation history for LLM context. The input indicator should change to show command mode, and results should be integrated into the conversation flow.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Command_Execution_Scenarios:
    Scenario_1:
      Title: "Dollar-sign command execution"
      Given: "Input box is empty and user types '$ls'"
      When: "User presses Enter"
      Then: "Command is executed in terminal"
      And: "Command and output are added to conversation"
      And: "LLM receives command context in next interaction"
      
    Scenario_2:
      Title: "Command mode visual indication"
      Given: "User types '$' in empty input box"
      When: "Dollar sign is entered"
      Then: "Input prompt changes to show command mode"
      And: "Visual indicator shows command context"
```

---

## US-2025-048
### Plugin Charm Icons and Visual Identity

```yaml
Story_Metadata:
  Story_ID: "US-2025-048"
  Epic_ID: "EPIC-VISUAL-EXPERIENCE"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Low"
  Size_Estimate: "3"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "4"
  Risk_Level: "Low"
  Dependencies_Identified: "US-2025-009 (Plugin Discovery)"
  Similar_Stories: "Icon systems, visual branding"
  Implementation_Suggestions: "Plugin icon registration and display system"
```

#### User Story Statement

**As a** user and plugin developer  
**I want** plugins to have visual charm icons that appear during startup and in status areas  
**So that** I can quickly identify plugins and the system has a more polished, branded appearance  

#### Story Context
Plugins should be able to register small charm icons (like 🧠 for LLM, 🏆 for achievements) that appear during startup sequence and in status displays. This provides visual identity and makes plugin identification easier.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Visual_Identity_Scenarios:
    Scenario_1:
      Title: "Plugin icons during startup"
      Given: "Plugins have registered charm icons"
      When: "Application starts and discovers plugins"
      Then: "Plugin icons appear in startup sequence"
      And: "Icons provide visual identity for each plugin"
      
    Scenario_2:
      Title: "Icons in status areas and displays"
      Given: "Plugins are active and have icons"
      When: "Status areas display plugin information"
      Then: "Plugin icons appear alongside status text"
      And: "Icons help identify plugin sources"
```

---

## US-2025-049
### Agent Communication and Management System

```yaml
Story_Metadata:
  Story_ID: "US-2025-049"
  Epic_ID: "EPIC-AGENT-SYSTEM"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Low"
  Size_Estimate: "21"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "9"
  Risk_Level: "High"
  Dependencies_Identified: "US-2025-042 (Slash Commands), Large plugin system"
  Similar_Stories: "Multi-agent systems, process management"
  Implementation_Suggestions: "Complex plugin with agent management and communication"
```

#### User Story Statement

**As a** power user  
**I want** to create, manage, and communicate with specialized AI agents using commands like `/agent CEO start`  
**So that** I can delegate specific tasks to domain-specific agents and enable inter-agent communication  

#### Story Context
This is a complex plugin system that allows creating specialized agents (CEO, Manager, Director) that can communicate with each other. Agents have dedicated folders in `.kollabor/agents/` with inbox systems. Commands like `/agent CEO start` launch agents in terminal sessions.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Agent_Creation_Scenarios:
    Scenario_1:
      Title: "Create new specialized agent"
      Given: "User types '/agent new create CEO agent'"
      When: "Command initiates agent creation workflow"
      Then: "Agent creation wizard guides user through setup"
      And: "Agent folder created in .kollabor/agents/"
      And: "Agent configuration and inbox system initialized"
      
    Scenario_2:
      Title: "Start agent in terminal session"
      Given: "User types '/agent CEO start'"
      When: "Command is executed"
      Then: "New terminal session launches with agent context"
      And: "Agent runs with configured profile and capabilities"
      
  Inter_Agent_Communication_Scenarios:
    Scenario_3:
      Title: "Agents can message each other"
      Given: "Multiple agents are active"
      When: "CEO agent sends message to Manager agent"
      Then: "Message is delivered to Manager agent's inbox"
      And: "Manager agent can respond through communication system"
```

---

## US-2025-050
### Context Engineering Plugin System

```yaml
Story_Metadata:
  Story_ID: "US-2025-050"
  Epic_ID: "EPIC-CONTEXT-ENGINEERING"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Medium"
  Size_Estimate: "13"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "8"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-013 (Core LLM System)"
  Similar_Stories: "Prompt engineering, context management"
  Implementation_Suggestions: "Plugin that integrates with core LLM system for dynamic context optimization"
```

#### User Story Statement

**As a** user working with LLMs  
**I want** intelligent context engineering that optimizes prompts and manages conversation context  
**So that** I get better LLM responses through improved prompt structure and relevant context inclusion  

#### Story Context
A plugin that enhances core LLM system interactions by engineering better prompts, managing context windows, and optimizing the information sent to LLMs. This includes prompt templates, context pruning, and intelligent context selection. Works as a preprocessing layer for the core LLM system.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Context_Optimization_Scenarios:
    Scenario_1:
      Title: "Prompt engineering and optimization"
      Given: "User sends query to LLM"
      When: "Context engineering plugin processes request"
      Then: "Prompt is optimized for better LLM performance"
      And: "Relevant context is included efficiently"
      
    Scenario_2:
      Title: "Context window management"
      Given: "Conversation history exceeds context limits"
      When: "Context engineering analyzes conversation"
      Then: "Most relevant context is preserved"
      And: "Less important context is intelligently pruned"
```

---

## US-2025-040
### Advanced Status Area Layout System

```yaml
Story_Metadata:
  Story_ID: "US-2025-040"
  Epic_ID: "EPIC-ADVANCED-TERMINAL"
  Project: "Kollabor CLI Interface"
  Created_Date: "2025-09-09"
  Created_By: "Claude Code"
  Status: "Backlog"
  Priority: "Medium"
  Size_Estimate: "8"
  
AI_Analysis:
  Generated_By: "Claude Code"
  Analysis_Date: "2025-09-09"
  Complexity_Score: "7"
  Risk_Level: "Medium"
  Dependencies_Identified: "US-2025-038 (Multi-row Navigation)"
  Similar_Stories: "Complex layouts, terminal multiplexers"
  Implementation_Suggestions: "Flexible layout system with area combination"
```

#### User Story Statement

**As a** power user  
**I want** to combine status areas A, B, and C and configure above/below input display sections  
**So that** I can create custom layouts that match my workflow and information needs  

#### Story Context
The terminal interface should support flexible layouts where status areas A, B, and C can be combined for full-width displays, and additional sections above input, below input, and below status can be configured for plugin output.

#### Acceptance Criteria
```yaml
Acceptance_Criteria:
  Layout_Configuration_Scenarios:
    Scenario_1:
      Title: "Combine status areas for full-width display"
      Given: "User configures status area layout"
      When: "Areas A+B+C are combined"
      Then: "Full terminal width is used for status display"
      And: "Plugin content spans entire width appropriately"
      
    Scenario_2:
      Title: "Configure above/below input sections"
      Given: "Plugins need display space above or below input"
      When: "Layout configuration is applied"
      Then: "Above input, below input, and below status sections are available"
      And: "Plugins can assign content to specific sections"
```

---

*This document now contains all critical foundation stories for system stability, interactive navigation, slash commands, security, and advanced features. Each story follows the template structure with comprehensive acceptance criteria and implementation guidance.*

---

*This document serves as the definitive guide for Chat App development, providing clear user-focused stories that drive implementation decisions and ensure delivery of genuine user value.*