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AgileBloom: AI-Powered Agile Discussion Facilitator

COMPUTATIONAL ARCHITECTURE OVERVIEW

┌─────────────────────────────────────────────────────────────────────────┐
│                          AGILEBLOOM SYSTEM                             │
│                                                                         │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────┐                │
│  │   LANDING   │───▶│    SETUP    │───▶│    CHAT     │                │
│  │   STAGE     │    │   STAGE     │    │ INTERFACE   │                │
│  │             │    │             │    │   STAGE     │                │
│  └─────────────┘    └─────────────┘    └─────────────┘                │
│                                                                         │
│  ┌─────────────────────────────────────────────────────────────────────┐ │
│  │                    EXPERT ORCHESTRATION LAYER                      │ │
│  │ ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐  │ │
│  │ │ENGINEER │  │ ARTIST  │  │LINGUIST │  │ SCRUM   │  │  USER   │  │ │
│  │ │  👨‍💻     │  │  🧑‍🎨   │  │  🧑‍✒️   │  │LEADER🤔│  │ SYSTEM  │  │ │
│  │ │PROCESS  │  │PROCESS  │  │PROCESS  │  │PROCESS  │  │PROCESS  │  │ │
│  │ └─────────┘  └─────────┘  └─────────┘  └─────────┘  └─────────┘  │ │
│  └─────────────────────────────────────────────────────────────────────┘ │
│                                                                         │
│  ┌─────────────────────────────────────────────────────────────────────┐ │
│  │                      AI PROVIDER LAYER                             │ │
│  │ ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐                │ │
│  │ │ GEMINI  │  │ OPENAI  │  │ MISTRAL │  │OPENROUTER│                │ │
│  │ │ API     │  │ API     │  │ API     │  │   API    │                │ │
│  │ └─────────┘  └─────────┘  └─────────┘  └─────────┘                │ │
│  └─────────────────────────────────────────────────────────────────────┘ │
│                                                                         │
│  ┌─────────────────────────────────────────────────────────────────────┐ │
│  │                        STATE LAYER                                 │ │
│  │ ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐  │ │
│  │ │MESSAGES │  │EXPERTS  │  │QUESTIONS│  │ TASKS   │  │STORIES  │  │ │
│  │ │  STORE  │  │  STORE  │  │  STORE  │  │  STORE  │  │  STORE  │  │ │
│  │ └─────────┘  └─────────┘  └─────────┘  └─────────┘  └─────────┘  │ │
│  └─────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘

SYSTEM SPECIFICATIONS

EXPERT PROCESS DEFINITIONS

ENGINEER (👨‍💻): Technical implementation computational unit

  • PRIMARY FUNCTION: Transform abstract requirements into executable code specifications
  • SPECIALIZATION DOMAINS: Python, Bash, Ansible, system architecture
  • COMPUTATIONAL ROLE: Technical feasibility analysis, implementation pathway generation

ARTIST (🧑‍🎨): User experience computational unit

  • PRIMARY FUNCTION: Transform functional requirements into user-centered design specifications
  • SPECIALIZATION DOMAINS: CSS, JavaScript, HTML, user interface design
  • COMPUTATIONAL ROLE: Visual design synthesis, user interaction modeling

LINGUIST (🧑‍✒️): Code quality computational unit

  • PRIMARY FUNCTION: Transform implementation specifications into optimal code patterns
  • SPECIALIZATION DOMAINS: Ruby, linguistic analysis, design patterns
  • COMPUTATIONAL ROLE: Code quality assurance, pattern recognition and optimization

SCRUM LEADER (🤔): Project management computational unit

  • PRIMARY FUNCTION: Transform project requirements into structured workflow specifications
  • SPECIALIZATION DOMAINS: Agile methodologies, backlog management, sprint planning
  • COMPUTATIONAL ROLE: Project coordination, task prioritization, workflow optimization

OPERATIONAL PIPELINE

INPUT → DISCUSSION → ANALYSIS → SYNTHESIS → OUTPUT
  ↓        ↓           ↓          ↓         ↓
TOPIC → EXPERT      → QUESTION → USER    → TASKS
       RESPONSES      TRACKING   STORIES

DETAILED PROCESSING FLOW

Data Flow Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                          DATA PROCESSING PIPELINE                      │
│                                                                         │
│  USER INPUT                                                             │
│      ↓                                                                  │
│  ┌─────────┐     ┌─────────┐     ┌─────────┐     ┌─────────┐          │
│  │COMMAND  │────▶│COMMAND  │────▶│EXPERT   │────▶│RESPONSE │          │
│  │PARSER   │     │ROUTER   │     │SELECTOR │     │GENERATOR│          │
│  └─────────┘     └─────────┘     └─────────┘     └─────────┘          │
│      ↓                ↓               ↓               ↓                │
│  ┌─────────┐     ┌─────────┐     ┌─────────┐     ┌─────────┐          │
│  │VALIDATION│     │CONTEXT  │     │AI API   │     │CONTENT  │          │
│  │PROCESS  │     │INJECTION│     │CALL     │     │PROCESSOR│          │
│  └─────────┘     └─────────┘     └─────────┘     └─────────┘          │
│      ↓                ↓               ↓               ↓                │
│  ┌─────────┐     ┌─────────┐     ┌─────────┐     ┌─────────┐          │
│  │ERROR    │     │MEMORY   │     │RESPONSE │     │UI       │          │
│  │HANDLER  │     │STORAGE  │     │PARSER   │     │RENDERER │          │
│  └─────────┘     └─────────┘     └─────────┘     └─────────┘          │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

COMMAND PROCESSING ARCHITECTURE

Command Execution Flow

graph TD
    A[USER INPUT] --> B{COMMAND PARSER}
    B -->|/ask| C[SEARCH INTEGRATION]
    B -->|/suggest| D[EXPERT DISPATCHER]
    B -->|/insight| E[ANALYSIS ENGINE]
    B -->|/direction| F[INSTRUCTION PROCESSOR]
    B -->|/elaborate| G[EXPERT SELECTOR]
    B -->|/show-work| H[WORK DISPLAY]
    B -->|/questions| I[QUESTION TRACKER]
    B -->|/stories| J[STORY GENERATOR]
    B -->|/backlog| K[BACKLOG MANAGER]
    B -->|/analyze| L[FISH ANALYZER]
    
    C --> M[GOOGLE SEARCH API]
    D --> N[ROUND-ROBIN EXPERT SELECTION]
    E --> N
    F --> N
    G --> O[TARGETED EXPERT RESPONSE]
    H --> O
    I --> P[QUESTION STATE MANAGEMENT]
    J --> Q[USER STORY SYNTHESIS]
    K --> R[BACKLOG HEALTH CHECK]
    L --> S[FISH FRAMEWORK APPLICATION]
    
    M --> T[SEARCH RESULTS INTEGRATION]
    N --> U[AI PROVIDER CALL]
    O --> U
    P --> V[UI STATE UPDATE]
    Q --> W[STORY COLLECTION UPDATE]
    R --> X[BACKLOG METRICS]
    S --> Y[ANALYSIS REPORT]
    
    T --> Z[CONTEXT INJECTION]
    U --> Z
    V --> AA[RENDER CYCLE]
    W --> AA
    X --> AA
    Y --> AA
    
    Z --> AA
    AA --> AB[USER INTERFACE UPDATE]
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State Management Architecture

graph LR
    A[ZUSTAND STORE] --> B[DISCUSSION STATE]
    A --> C[EXPERT STATE]
    A --> D[QUESTION STATE]
    A --> E[TASK STATE]
    A --> F[STORY STATE]
    A --> G[MEMORY STATE]
    A --> H[CONFIG STATE]
    
    B --> I[MESSAGES ARRAY]
    B --> J[ACTIVE DISCUSSION]
    B --> K[AUTO MODE STATUS]
    
    C --> L[EXPERT DEFINITIONS]
    C --> M[EXPERT ROTATION]
    C --> N[RESPONSE TRACKING]
    
    D --> O[TRACKED QUESTIONS]
    D --> P[QUESTION STATUS]
    D --> Q[QUESTION METADATA]
    
    E --> R[TASK COLLECTION]
    E --> S[TASK PRIORITIES]
    E --> T[TASK COMPLETION]
    
    F --> U[USER STORIES]
    F --> V[STORY STATUS]
    F --> W[STORY BREAKDOWN]
    
    G --> X[CONTEXT MEMORY]
    G --> Y[MEMORY ENTRIES]
    G --> Z[MEMORY LIMITS]
    
    H --> AA[AI PROVIDER CONFIG]
    H --> AB[MODEL SELECTION]
    H --> AC[API KEY VALIDATION]
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INSTALLATION & SYSTEM SETUP

COMPUTATIONAL REQUIREMENTS

  • Node.js Runtime: Version 18+ (JavaScript V8 engine)
  • Package Manager: npm/yarn (dependency resolution)
  • AI Provider API: Google Gemini (preferred) | OpenAI | Mistral | OpenRouter

SYSTEM INITIALIZATION PROTOCOL

# Repository cloning operation
git clone https://github.com/your-username/agilebloom.git
cd agilebloom

# Dependency installation process
npm install

# Environment configuration setup
cp .env.example .env.local

# API key injection
echo "GEMINI_API_KEY=your_api_key_here" >> .env.local

# Development server initialization
npm run dev

ENVIRONMENT CONFIGURATION MATRIX

┌─────────────────────┬────────────────────────────────────────────────────┐
│ VARIABLE            │ COMPUTATIONAL FUNCTION                            │
├─────────────────────┼────────────────────────────────────────────────────┤
│ GEMINI_API_KEY      │ Google Gemini API authentication token           │
│ OPENAI_API_KEY      │ OpenAI GPT API authentication token              │
│ MISTRAL_API_KEY     │ Mistral AI API authentication token              │
│ OPENROUTER_API_KEY  │ OpenRouter API authentication token              │
└─────────────────────┴────────────────────────────────────────────────────┘

OPERATIONAL USAGE PROTOCOL

SYSTEM INITIALIZATION SEQUENCE

  1. LAUNCH PROCESS: Execute npm run dev → Navigate to http://localhost:5173
  2. CONFIGURATION PHASE: Configure AI provider authentication and model selection
  3. DISCUSSION INITIALIZATION: Input project topic and contextual parameters
  4. EXPERT ENGAGEMENT: Deploy command-based expert orchestration

COMMAND EXECUTION MATRIX

┌─────────────────────┬────────────────────────────────────────────────────┐
│ COMMAND             │ COMPUTATIONAL OPERATION                            │
├─────────────────────┼────────────────────────────────────────────────────┤
│ /ask {query}        │ Search integration + expert analysis synthesis    │
│ /suggest {proposal} │ Expert evaluation + feedback generation            │
│ /insight {data}     │ Expert analysis + insight synthesis               │
│ /direction {order}  │ Expert coordination + execution planning           │
│ /continue           │ Expert discussion continuation trigger             │
│ /elaborate {expert} │ Targeted expert deep-dive analysis                │
│ /show-work {expert} │ Expert work state display operation               │
│ /breakdown {id}     │ User story decomposition into task units          │
│ /questions          │ Question state management interface                │
│ /stories            │ User story synthesis from question data           │
│ /backlog            │ Backlog health metrics and analysis               │
│ /analyze {id}       │ FISH framework systematic analysis                │
└─────────────────────┴────────────────────────────────────────────────────┘

ADVANCED OPERATIONAL MODES

AUTO MODE COMPUTATIONAL PROCESS

┌─────────────────────────────────────────────────────────────────────────┐
│                        AUTO MODE ARCHITECTURE                          │
│                                                                         │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐              │
│  │  AUTO MODE  │────▶│   DELAY     │────▶│   EXPERT    │              │
│  │  TRIGGER    │     │ PROCESSOR   │     │ ACTIVATION  │              │
│  └─────────────┘     └─────────────┘     └─────────────┘              │
│         ↓                    ↓                    ↓                    │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐              │
│  │ TOGGLE      │     │ CONFIGURABLE│     │ AUTOMATIC   │              │
│  │ STATUS      │     │ TIMING      │     │ RESPONSE    │              │
│  │ MONITORING  │     │ (3-30 SEC)  │     │ GENERATION  │              │
│  └─────────────┘     └─────────────┘     └─────────────┘              │
└─────────────────────────────────────────────────────────────────────────┘

FILE UPLOAD PROCESSING SPECIFICATIONS

  • IMAGE FORMATS: JPG, PNG, GIF, WebP (maximum 5MB binary data)
  • TEXT FORMATS: .txt, .md files (contextual data injection)
  • UPLOAD MECHANISM: Attachment interface OR drag-and-drop operation
  • PROCESSING PIPELINE: File validation → Content extraction → AI analysis integration

FISH ANALYSIS COMPUTATIONAL FRAMEWORK

┌─────────────────────────────────────────────────────────────────────────┐
│                          FISH ANALYSIS MATRIX                          │
│                                                                         │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐              │
│  │FUNCTIONAL   │     │INTERACTIONAL│     │  SEMANTIC   │              │
│  │  ANALYSIS   │     │  ANALYSIS   │     │  ANALYSIS   │              │
│  │             │     │             │     │             │              │
│  │Process      │     │Dynamics     │     │Certainty    │              │
│  │Evaluation   │     │Dependencies │     │Commitment   │              │
│  └─────────────┘     └─────────────┘     └─────────────┘              │
│         ↓                    ↓                    ↓                    │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐              │
│  │HIERARCHICAL │     │  SYNTHESIS  │     │  ANALYSIS   │              │
│  │  ANALYSIS   │     │  PROCESSOR  │     │  REPORT     │              │
│  │             │     │             │     │ GENERATION  │              │
│  │Communication│     │Integration  │     │Structured   │              │
│  │Transparency │     │Framework    │     │Output       │              │
│  └─────────────┘     └─────────────┘     └─────────────┘              │
└─────────────────────────────────────────────────────────────────────────┘

COMPUTATIONAL USE CASE SCENARIOS

SCENARIO 1: SOLO DEVELOPER PROJECT INITIALIZATION

OPERATIONAL PARAMETERS:

  • TARGET SYSTEM: Personal finance tracking web application
  • TECHNOLOGY STACK: React-based frontend architecture
  • FUNCTIONAL REQUIREMENTS: Expense tracking, income monitoring, budget goal management

COMMAND EXECUTION SEQUENCE:

# System initialization
TOPIC_INPUT: "Personal finance tracking web application"
CONTEXT_INPUT: "React application for expense/income/budget tracking"

# Expert orchestration protocol
/ask What are the core features needed for an MVP?
/suggest Starting with expense tracking before adding advanced features
/direction Focus on user experience and data privacy
/questions                # Question state extraction
/stories                  # User story synthesis
/breakdown {story_id}     # Task decomposition

COMPUTATIONAL OUTPUT METRICS:

  • USER STORIES: 8-12 structured narrative specifications
  • DEVELOPMENT TASKS: 25-40 atomic implementation units
  • PRIORITY MATRIX: Implementation sequence optimization
  • ARCHITECTURE SPECIFICATIONS: Technical design recommendations

SCENARIO 2: TEAM LEAD FEATURE PLANNING PROTOCOL

OPERATIONAL PARAMETERS:

  • TARGET SYSTEM: Multi-factor authentication implementation
  • SECURITY REQUIREMENTS: 2FA integration with existing user system
  • CONSTRAINT SPECIFICATIONS: Backward compatibility maintenance

COMMAND EXECUTION SEQUENCE:

# Security analysis initialization
TOPIC_INPUT: "Multi-factor authentication implementation"
CONTEXT_INPUT: "2FA integration with security and UX optimization"

# Expert consultation protocol
/ask What security considerations should we prioritize?
/elaborate Engineer     # Technical implementation analysis
/elaborate Artist       # UX impact assessment
/insight Current users prefer email-based verification
/direction Must maintain backward compatibility
/analyze {auth_story}   # FISH framework analysis

COMPUTATIONAL OUTPUT METRICS:

  • SECURITY STORIES: Risk-focused user narratives
  • UX FLOWS: Authentication user experience specifications
  • IMPLEMENTATION TASKS: Technical execution units
  • RISK ASSESSMENT: Mitigation strategy framework

SCENARIO 3: STARTUP PRODUCT DISCOVERY ENGINE

OPERATIONAL PARAMETERS:

  • TARGET SYSTEM: AI-powered task prioritization tool
  • MARKET ANALYSIS: Knowledge worker productivity optimization
  • VALIDATION REQUIREMENTS: Market fit assessment

COMMAND EXECUTION SEQUENCE:

# Product discovery initialization
TOPIC_INPUT: "AI-powered task prioritization tool"
CONTEXT_INPUT: "Knowledge worker productivity and high-impact activity focus"

# Market validation protocol
/ask What problems do current productivity tools fail to solve?
/dataset "User research shows 73% struggle with task prioritization"
/insight Users want automation but fear losing control
/suggest Gradual AI assistance with user override capabilities
/backlog               # Product backlog health analysis
/sprint-planning       # Development sprint initialization

COMPUTATIONAL OUTPUT METRICS:

  • MARKET STORIES: Validated user narrative specifications
  • FEATURE MATRIX: Priority-based functionality framework
  • PRODUCT ROADMAP: Development timeline optimization
  • SPRINT TASKS: Implementation-ready development units

SCENARIO 4: OPEN SOURCE OPTIMIZATION ANALYSIS

OPERATIONAL PARAMETERS:

  • TARGET SYSTEM: Performance optimization for large dataset processing
  • ISSUE SPECIFICATION: GitHub issue #1247 - API response degradation
  • PERFORMANCE METRICS: 50ms (100 records) → 8s (10k records)

COMMAND EXECUTION SEQUENCE:

# Performance analysis initialization
TOPIC_INPUT: "Performance optimization for large dataset processing"
CONTEXT_INPUT: "GitHub issue #1247 - API response times degrade with 10k+ records"

# Technical deep-dive protocol
/ask What are the primary bottlenecks in large dataset handling?
/debug "API response times: 50ms (100 records) -> 8s (10k records)"
/elaborate Engineer     # Technical solution generation
/elaborate Linguist     # Code quality analysis
/show-work Engineer     # Implementation approach display

COMPUTATIONAL OUTPUT METRICS:

  • ROOT CAUSE ANALYSIS: Bottleneck identification matrix
  • SOLUTION OPTIONS: Technical implementation alternatives
  • TASK BREAKDOWN: Implementation unit specifications
  • VALIDATION STRATEGY: Testing and verification framework

SCENARIO 5: LEARNING PROJECT ARCHITECTURE EXPLORATION

OPERATIONAL PARAMETERS:

  • TARGET SYSTEM: Microservices architecture for e-commerce platform
  • EDUCATIONAL OBJECTIVE: Monolithic to microservices conversion
  • LEARNING FRAMEWORK: Best practices acquisition

COMMAND EXECUTION SEQUENCE:

# Educational exploration initialization
TOPIC_INPUT: "Microservices architecture for e-commerce platform"
CONTEXT_INPUT: "Converting monolithic app to microservices, learning best practices"

# Knowledge acquisition protocol
/ask What are the key principles of microservice design?
/suggest Starting with user service and product catalog separation
/elaborate Scrum Leader  # Project management implications
/insight Database separation will be the biggest challenge
/game Engineer, Linguist, What would Martin Fowler say about our approach?

COMPUTATIONAL OUTPUT METRICS:

  • EDUCATIONAL STORIES: Learning-focused user narratives
  • HANDS-ON TASKS: Practical implementation exercises
  • BEST PRACTICE STEPS: Implementation methodology framework
  • COMPLEXITY ROADMAP: Progressive learning pathway optimization

TECHNICAL ARCHITECTURE SPECIFICATIONS

STATE MANAGEMENT COMPUTATIONAL FRAMEWORK

graph TB
    A[ZUSTAND STORE] --> B[CENTRALIZED STATE MANAGEMENT]
    B --> C[DISCUSSION TRACKING]
    B --> D[PERSISTENT MEMORY]
    B --> E[RATE LIMITING PROTECTION]
    
    C --> F[MESSAGE ARRAY]
    C --> G[EXPERT ROTATION STATE]
    C --> H[AUTO MODE STATUS]
    
    D --> I[CONTEXT RETENTION]
    D --> J[SESSION PERSISTENCE]
    D --> K[MEMORY ENTRY LIMITS]
    
    E --> L[API CALL THROTTLING]
    E --> M[REQUEST QUEUE MANAGEMENT]
    E --> N[BACKOFF STRATEGIES]
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AI INTEGRATION COMPUTATIONAL ARCHITECTURE

┌─────────────────────────────────────────────────────────────────────────┐
│                       AI PROVIDER INTEGRATION LAYER                    │
│                                                                         │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐   │
│  │   GOOGLE    │  │   OPENAI    │  │   MISTRAL   │  │ OPENROUTER  │   │
│  │   GEMINI    │  │     GPT     │  │     AI      │  │  GATEWAY    │   │
│  │             │  │             │  │             │  │             │   │
│  │ Flash/Pro/  │  │ GPT-3.5/4   │  │ 7B/8x7B     │  │ Multi-Model │   │
│  │ Lite Models │  │ Models      │  │ Models      │  │ Access      │   │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘   │
│         ↓                ↓                ↓                ↓           │
│  ┌─────────────────────────────────────────────────────────────────┐   │
│  │                 UNIFIED API INTERFACE                          │   │
│  │ ┌─────────────┐  ┌─────────────┐  ┌─────────────┐            │   │
│  │ │   IMAGE     │  │    JSON     │  │   ERROR     │            │   │
│  │ │  SUPPORT    │  │  RESPONSE   │  │  RECOVERY   │            │   │
│  │ │             │  │ STRUCTURED  │  │ EXPONENTIAL │            │   │
│  │ │ Visual      │  │ Expert      │  │ Backoff     │            │   │
│  │ │ Analysis    │  │ Outputs     │  │ Retry Logic │            │   │
│  │ └─────────────┘  └─────────────┘  └─────────────┘            │   │
│  └─────────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────────┘

COMPONENT STRUCTURE HIERARCHY

┌─────────────────────────────────────────────────────────────────────────┐
│                      REACT COMPONENT ARCHITECTURE                       │
│                                                                         │
│  ┌─────────────────────────────────────────────────────────────────────┐│
│  │                            ROOT LAYER                               ││
│  │ ┌─────────────┐     ┌─────────────┐     ┌─────────────┐             ││
│  │ │    APP      │────▶│   ROUTER    │────▶│   STAGE     │           ││
│  │ │ COMPONENT   │     │  HANDLER    │     │  MANAGER    │             ││
│  │ └─────────────┘     └─────────────┘     └─────────────┘             ││
│  └─────────────────────────────────────────────────────────────────────┘│
│                                    ↓                                    │
│  ┌─────────────────────────────────────────────────────────────────────┐│
│  │                        INTERFACE LAYER                              ││
│  │ ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  │
│  │ │    CHAT     │  │   SETUP     │  │  HEADER     │  │  SIDEBAR    │  │
│  │ │ INTERFACE   │  │    PAGE     │  │ COMPONENT   │  │ COMPONENTS  │  │
│  │ │             │  │             │  │             │  │             │  │
│  │ │Conversation │  │AI Provider  │  │Navigation   │  │Question/Task│  │
│  │ │Management   │  │Config       │  │Controls     │  │Story Mgmt   │  │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘ │
│  └─────────────────────────────────────────────────────────────────────┘ │
│                                    ↓                                    │
│  ┌─────────────────────────────────────────────────────────────────────┐ │
│  │                         SERVICE LAYER                               │ │
│  │ ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐  │
│  │ │   HOOKS     │  │  SERVICES   │  │    STORE    │  │    TYPES    │  │
│  │ │             │  │             │  │             │  │             │  │
│  │ │Custom React │  │AI Provider  │  │Zustand      │  │TypeScript   │  │
│  │ │Hooks        │  │Integrations │  │State Mgmt   │  │Definitions  │  │
│  └─────────────┘  └─────────────┘  └─────────────┘  └─────────────┘ │
│  └─────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘

DEPENDENCY COMPUTATIONAL MATRIX

┌─────────────────────┬────────────────────────────────────────────────────┐
│ DEPENDENCY          │ COMPUTATIONAL FUNCTION                             │
├─────────────────────┼────────────────────────────────────────────────────┤
│ react@19.1.0        │ Core UI rendering engine                           │
│ react-dom@19.1.0    │ DOM manipulation interface                         │
│ zustand@5.0.5       │ State management computational framework           │
│ uuid@11.1.0         │ Unique identifier generation                       │
│ @google/genai@1.4.0 │ Google Gemini API integration                      │
│ ai@3.2.36           │ AI provider abstraction layer                      │
│ @ai-sdk/mistral     │ Mistral AI integration interface                   │
│ openai@4.52.7       │ OpenAI API integration interface                   │
│ lucide-react@0.513  │ Icon component library                             │
│ typescript@5.7.2    │ Type safety and compilation                        │
│ vite@6.2.0          │ Build system and development server                │
└─────────────────────┴────────────────────────────────────────────────────┘

DEVELOPMENT OPERATIONAL PROTOCOLS

BUILD SYSTEM EXECUTION COMMANDS

┌─────────────────────┬────────────────────────────────────────────────────┐
│ COMMAND             │ COMPUTATIONAL OPERATION                            │
├─────────────────────┼────────────────────────────────────────────────────┤
│ npm run dev         │ Development server initialization (port 5173)      │
│ npm run build       │ Production bundle compilation and optimization     │
│ npm run preview     │ Production build verification and testing          │
└─────────────────────┴────────────────────────────────────────────────────┘

CONTRIBUTION COMPUTATIONAL WORKFLOW

graph LR
    A[REPOSITORY FORK] --> B[FEATURE BRANCH CREATION]
    B --> C[CODE IMPLEMENTATION]
    C --> D[COMMIT GENERATION]
    D --> E[BRANCH PUSH OPERATION]
    E --> F[PULL REQUEST CREATION]
    F --> G[CODE REVIEW PROCESS]
    G --> H[MERGE INTEGRATION]
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BRANCH CREATION PROTOCOL:

git checkout -b feature/computational-enhancement

COMMIT MESSAGE SPECIFICATION:

git commit -m 'feat: implement expert orchestration optimization'

REMOTE PUSH OPERATION:

git push origin feature/computational-enhancement

TROUBLESHOOTING COMPUTATIONAL DIAGNOSTICS

ERROR RESOLUTION MATRIX

┌─────────────────────┬────────────────────────────────────────────────────┐
│ ERROR TYPE          │ DIAGNOSTIC PROTOCOL                                │
├─────────────────────┼────────────────────────────────────────────────────┤
│ AI_NOT_RESPONDING   │ 1. API key validation                              │
│                     │ 2. Network connectivity verification               │
│                     │ 3. Browser console error analysis                  │
├─────────────────────┼────────────────────────────────────────────────────┤
│ RATE_LIMITING       │ 1. Built-in protection: 5 messages/10 seconds      │
│                     │ 2. Cooldown period enforcement                     │
│                     │ 3. API plan upgrade consideration                  │
├─────────────────────┼────────────────────────────────────────────────────┤
│ MEMORY_OVERFLOW     │ 1. `/clear` command execution                      │
│                     │ 2. Application restart procedure                   │
│                     │ 3. Memory context limit: 20 entries                │
├─────────────────────┼────────────────────────────────────────────────────┤
│ FILE_UPLOAD_FAILURE │ 1. File size validation: <5MB                      │
│                     │ 2. Format verification: PNG/JPG/GIF/WebP/TXT/MD    │
│                     │ 3. Browser console error inspection                │
└─────────────────────┴────────────────────────────────────────────────────┘

SYSTEM PERFORMANCE MONITORING

┌─────────────────────────────────────────────────────────────────────────┐
│                      PERFORMANCE METRICS DASHBOARD                      │
│                                                                         │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐                │
│  │ API LATENCY │     │ MEMORY USAGE│     │ERROR RATES  │                │
│  │             │     │             │     │             │                │
│  │ <2000ms     │     │ <100MB      │     │ <1%         │                │
│  │ Target      │     │ Target      │     │ Target      │                │
│  └─────────────┘     └─────────────┘     └─────────────┘                │
│         ↓                    ↓                    ↓                     │
│  ┌─────────────┐     ┌─────────────┐     ┌─────────────┐                │
│  │ RESPONSE    │     │ STATE SIZE  │     │ UPTIME      │                │
│  │ TIME        │     │ MONITORING  │     │ TRACKING    │                │
│  │ TRACKING    │     │             │     │             │                │
│  └─────────────┘     └─────────────┘     └─────────────┘                │
└─────────────────────────────────────────────────────────────────────────┘

LICENSING AND SUPPORT SPECIFICATIONS

LICENSE: MIT License - Complete terms available in LICENSE file

SUPPORT CHANNELS:

  • GitHub Issues: Bug reports and feature request submissions
  • GitHub Discussions: Community Q&A and collaborative ideation
  • Documentation: Comprehensive implementation guides in /docs directory

SYSTEM IDENTIFICATION

┌─────────────────────────────────────────────────────────────────────────┐
│                          AGILEBLOOM SYSTEM                              │
│                                                                         │
│  COMPUTATIONAL MISSION: Transform abstract project concepts into        │
│  structured, implementable development specifications through           │
│  AI-powered expert orchestration and systematic analysis frameworks.    │
│                                                                         │
│  PRIMARY FUNCTION: Agile methodology automation via artificial          │
│  intelligence collaborative discussion facilitation.                    │
└─────────────────────────────────────────────────────────────────────────┘

AGILEBLOOM: AI-Powered Computational Framework for Agile Development Orchestration

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