┌─────────────────────────────────────────────────────────────────────────┐
│ 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 │ │ │
│ │ └─────────┘ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │ │
│ └─────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘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
INPUT → DISCUSSION → ANALYSIS → SYNTHESIS → OUTPUT
↓ ↓ ↓ ↓ ↓
TOPIC → EXPERT → QUESTION → USER → TASKS
RESPONSES TRACKING STORIES┌─────────────────────────────────────────────────────────────────────────┐
│ 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 │ │
│ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────┘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]
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]
- Node.js Runtime: Version 18+ (JavaScript V8 engine)
- Package Manager: npm/yarn (dependency resolution)
- AI Provider API: Google Gemini (preferred) | OpenAI | Mistral | OpenRouter
# 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┌─────────────────────┬────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────┴────────────────────────────────────────────────────┘- LAUNCH PROCESS: Execute
npm run dev→ Navigate tohttp://localhost:5173 - CONFIGURATION PHASE: Configure AI provider authentication and model selection
- DISCUSSION INITIALIZATION: Input project topic and contextual parameters
- EXPERT ENGAGEMENT: Deploy command-based expert orchestration
┌─────────────────────┬────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────┴────────────────────────────────────────────────────┘┌─────────────────────────────────────────────────────────────────────────┐
│ AUTO MODE ARCHITECTURE │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ AUTO MODE │────▶│ DELAY │────▶│ EXPERT │ │
│ │ TRIGGER │ │ PROCESSOR │ │ ACTIVATION │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ ↓ ↓ ↓ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ TOGGLE │ │ CONFIGURABLE│ │ AUTOMATIC │ │
│ │ STATUS │ │ TIMING │ │ RESPONSE │ │
│ │ MONITORING │ │ (3-30 SEC) │ │ GENERATION │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘- 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 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 │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
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 decompositionCOMPUTATIONAL 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
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 analysisCOMPUTATIONAL OUTPUT METRICS:
- SECURITY STORIES: Risk-focused user narratives
- UX FLOWS: Authentication user experience specifications
- IMPLEMENTATION TASKS: Technical execution units
- RISK ASSESSMENT: Mitigation strategy framework
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 initializationCOMPUTATIONAL 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
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 displayCOMPUTATIONAL OUTPUT METRICS:
- ROOT CAUSE ANALYSIS: Bottleneck identification matrix
- SOLUTION OPTIONS: Technical implementation alternatives
- TASK BREAKDOWN: Implementation unit specifications
- VALIDATION STRATEGY: Testing and verification framework
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
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]
┌─────────────────────────────────────────────────────────────────────────┐
│ 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 │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │
│ └─────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘┌─────────────────────────────────────────────────────────────────────────┐
│ 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 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 │
└─────────────────────┴────────────────────────────────────────────────────┘┌─────────────────────┬────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────┴────────────────────────────────────────────────────┘
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]
BRANCH CREATION PROTOCOL:
git checkout -b feature/computational-enhancementCOMMIT MESSAGE SPECIFICATION:
git commit -m 'feat: implement expert orchestration optimization'REMOTE PUSH OPERATION:
git push origin feature/computational-enhancement┌─────────────────────┬────────────────────────────────────────────────────┐
│ 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 │
└─────────────────────┴────────────────────────────────────────────────────┘┌─────────────────────────────────────────────────────────────────────────┐
│ PERFORMANCE METRICS DASHBOARD │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ API LATENCY │ │ MEMORY USAGE│ │ERROR RATES │ │
│ │ │ │ │ │ │ │
│ │ <2000ms │ │ <100MB │ │ <1% │ │
│ │ Target │ │ Target │ │ Target │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ ↓ ↓ ↓ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ RESPONSE │ │ STATE SIZE │ │ UPTIME │ │
│ │ TIME │ │ MONITORING │ │ TRACKING │ │
│ │ TRACKING │ │ │ │ │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘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
/docsdirectory
┌─────────────────────────────────────────────────────────────────────────┐
│ 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