aivibe 1.0.0

AI Vibe - Complete AI Agent Training Package for VibeKaro.ai Platform with Flutter, Dart, Kotlin, Python, AWS expertise

AIVibe - AI Agent Training Package

AIVibe is the comprehensive AI agent training package for VibeKaro.ai - the AI-powered full-stack development platform. This package contains complete knowledge modules for training AI agents with production-grade coding standards.

Features

• Complete Flutter/Dart Knowledge - Latest Flutter 3.24+, Dart 3.5+ standards
• Multi-Platform Support - iOS 18 SDK, Android API 36, Web hybrid
• Backend Expertise - Kotlin, Python, JavaScript, PostgreSQL
• Cloud Services - AWS, Google Cloud, Firebase integration patterns
• SDLC Framework - 10-phase waterfall model with agent roles
• Coding Standards - Zero-tolerance, production-ready patterns
• Auto-Training - Daily scheduled training with evaluation
• Scorecard System - Training effectiveness tracking

Installation

pip install aivibe

Quick Start

Train AI Agent

from aivibe import AIVibeTrainer

trainer = AIVibeTrainer()
result = trainer.train_agent("aikutty")
print(f"Training Score: {result.score}%")

Schedule Daily Training

aivibe-schedule --agent aikutty --time 02:00

Evaluate Agent Knowledge

aivibe-evaluate --agent aikutty --output scorecard.json

Knowledge Modules

Module	Description	Version
flutter	Flutter framework, widgets, state management	3.24.x
dart	Dart language, null safety, patterns	3.5.x
kotlin	Kotlin coroutines, Android development	2.0.x
python	Python 3.12+, async patterns	3.12.x
javascript	ES2024, TypeScript 5.5	ES2024
postgresql	PostgreSQL 16, Aurora patterns	16.x
aws	AWS services, CDK, Lambda	Latest
gcloud	Google Cloud, Firebase	Latest
sdlc	10-phase SDLC, agent roles	1.0

Agent Roles by SDLC Phase

Phase	AiVedha Role	AiKutty Role
1. Requirements	Gather & clarify	Document structure
2. System Design	Architecture review	Component design
3. UI/UX Design	User feedback	Widget specifications
4. Database Design	Schema review	Migration scripts
5. API Development	Endpoint validation	Implementation
6. Flutter Dev	Code review	Full implementation
7. Testing	Test planning	Test execution
8. Integration	Integration review	E2E testing
9. Deployment	Release coordination	Build & deploy
10. Maintenance	User support	Bug fixes

Coding Standards

Zero-Tolerance Policy

• No lint errors or warnings
• No type mismatches
• No deprecated dependencies
• No duplicate code
• Complete error handling
• Full documentation

Naming Conventions

• camelCase for variables and functions
• PascalCase for classes and types
• snake_case for file names
• SCREAMING_SNAKE_CASE for constants

Training Evaluation

Training sessions are evaluated on:

1. Syntax Correctness (25%) - Code compiles without errors
2. Best Practices (25%) - Follows recommended patterns
3. Security Compliance (20%) - No vulnerabilities
4. Performance (15%) - Optimized code
5. Documentation (15%) - Complete dartdocs

Configuration

Create aivibe.yaml in your project:

agent: aikutty
schedule:
  enabled: true
  time: "02:00"
  timezone: "UTC"
evaluation:
  min_score: 85
  report_path: ./reports/
training:
  modules:
    - flutter
    - dart
    - aws
    - sdlc

API Reference

AIVibeTrainer

class AIVibeTrainer:
    def train_agent(self, agent_name: str, modules: list = None) -> TrainingResult
    def evaluate_agent(self, agent_name: str) -> EvaluationResult
    def get_scorecard(self, agent_name: str) -> Scorecard
    def schedule_training(self, agent_name: str, cron: str) -> None

TrainingResult

@dataclass
class TrainingResult:
    agent: str
    score: float
    modules_trained: list[str]
    duration_seconds: float
    timestamp: datetime
    details: dict

License

MIT License - See LICENSE for details.

Support

• Documentation: https://docs.vibekaro.ai
• Issues: https://github.com/aicippy/aivibe/issues
• Email: [e-mail]

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Built with excellence by AICippy Technologies for VibeKaro.ai