Claude、ChatGPT、Cursor三剑客：构建我的AI辅助开发工作流

// AI工具协作配置接口
interface AIToolConfig {
  name: string;
  strengths: string[];
  bestUseCases: string[];
  integrationPoints: string[];
}

// 工具协作策略
class AIWorkflowOrchestrator {
  private tools: Map<string, AIToolConfig> = new Map();
  
  constructor() {
    this.initializeTools();
  }
  
  private initializeTools(): void {
    // Claude配置 - 深度思考和架构设计
    this.tools.set('claude', {
      name: 'Claude',
      strengths: ['深度分析', '架构设计', '代码审查'],
      bestUseCases: ['系统设计', '技术选型', '复杂问题分析'],
      integrationPoints: ['项目规划', '代码审查', '文档生成']
    });
    
    // ChatGPT配置 - 快速问答和代码生成
    this.tools.set('chatgpt', {
      name: 'ChatGPT',
      strengths: ['快速响应', '代码生成', '问题解答'],
      bestUseCases: ['调试辅助', '代码解释', '快速原型'],
      integrationPoints: ['开发过程', '问题解决', '学习辅助']
    });
    
    // Cursor配置 - 实时编码辅助
    this.tools.set('cursor', {
      name: 'Cursor',
      strengths: ['实时补全', 'IDE集成', '上下文感知'],
      bestUseCases: ['代码编写', '重构辅助', '语法检查'],
      integrationPoints: ['编码阶段', '代码优化', '实时提示']
    });
  }
  
  // 根据任务类型选择最佳工具
  selectOptimalTool(taskType: string): string {
    const taskToolMapping = {
      'architecture': 'claude',
      'debugging': 'chatgpt',
      'coding': 'cursor',
      'review': 'claude',
      'learning': 'chatgpt'
    };
    
    return taskToolMapping[taskType] || 'chatgpt';
  }
}

# Claude辅助的代码审查示例
class CodeReviewAssistant:
    def __init__(self):
        self.review_criteria = {
            'performance': ['时间复杂度', '空间复杂度', '算法效率'],
            'maintainability': ['代码可读性', '模块化程度', '注释质量'],
            'security': ['输入验证', '权限控制', '数据加密'],
            'scalability': ['并发处理', '负载能力', '扩展性设计']
        }
    
    def analyze_code_quality(self, code_snippet: str) -> dict:
        """
        使用Claude进行代码质量分析
        """
        analysis_result = {
            'score': 0,
            'issues': [],
            'suggestions': [],
            'best_practices': []
        }
        
        # 性能分析
        performance_issues = self._check_performance(code_snippet)
        if performance_issues:
            analysis_result['issues'].extend(performance_issues)
        
        # 可维护性检查
        maintainability_score = self._check_maintainability(code_snippet)
        analysis_result['score'] += maintainability_score
        
        # 安全性审查
        security_warnings = self._check_security(code_snippet)
        if security_warnings:
            analysis_result['issues'].extend(security_warnings)
        
        return analysis_result
    
    def _check_performance(self, code: str) -> list:
        """检查性能相关问题"""
        issues = []
        
        # 检查嵌套循环
        if 'for' in code and code.count('for') > 1:
            issues.append({
                'type': 'performance',
                'severity': 'medium',
                'message': '检测到嵌套循环，可能影响性能',
                'suggestion': '考虑使用更高效的算法或数据结构'
            })
        
        return issues
    
    def _check_maintainability(self, code: str) -> int:
        """评估代码可维护性"""
        score = 100
        
        # 检查函数长度
        lines = code.split('\n')
        if len(lines) > 50:
            score -= 20
        
        # 检查注释比例
        comment_lines = [line for line in lines if line.strip().startswith('#')]
        comment_ratio = len(comment_lines) / len(lines) if lines else 0
        
        if comment_ratio < 0.1:
            score -= 15
        
        return max(score, 0)

# Claude生成的API文档示例

## 用户管理API

### 概述
用户管理API提供了完整的用户生命周期管理功能，包括注册、认证、权限管理等核心功能。

### 认证方式
- JWT Token认证
- OAuth 2.0支持
- API Key认证（管理员接口）

### 接口列表

#### 1. 用户注册
**POST** `/api/v1/users/register`

**请求参数：**
```json
{
  "username": "string",
  "email": "string",
  "password": "string",
  "profile": {
    "firstName": "string",
    "lastName": "string"
  }
}

{
  "success": true,
  "data": {
    "userId": "12345",
    "token": "jwt_token_here"
  }
}

// ChatGPT生成的React Hook示例
import { useState, useEffect, useCallback } from 'react';

/**
 * 自定义Hook：用于管理API请求状态
 * @param {Function} apiFunction - API请求函数
 * @param {Array} dependencies - 依赖数组
 * @returns {Object} 包含数据、加载状态、错误信息的对象
 */
export const useApiRequest = (apiFunction, dependencies = []) => {
  const [data, setData] = useState(null);
  const [loading, setLoading] = useState(false);
  const [error, setError] = useState(null);
  
  // 执行API请求的函数
  const executeRequest = useCallback(async (...args) => {
    try {
      setLoading(true);
      setError(null);
      
      const result = await apiFunction(...args);
      setData(result);
      
      return result;
    } catch (err) {
      setError(err.message || '请求失败');
      throw err;
    } finally {
      setLoading(false);
    }
  }, [apiFunction]);
  
  // 重置状态
  const reset = useCallback(() => {
    setData(null);
    setError(null);
    setLoading(false);
  }, []);
  
  // 自动执行请求（如果有依赖变化）
  useEffect(() => {
    if (dependencies.length > 0) {
      executeRequest();
    }
  }, dependencies);
  
  return {
    data,
    loading,
    error,
    execute: executeRequest,
    reset
  };
};

// 使用示例
const UserProfile = ({ userId }) => {
  const {
    data: userProfile,
    loading,
    error,
    execute: fetchProfile
  } = useApiRequest(
    (id) => fetch(`/api/users/${id}`).then(res => res.json()),
    [userId]
  );
  
  if (loading) return <div>加载中...</div>;
  if (error) return <div>错误: {error}</div>;
  
  return (
    <div>
      <h2>{userProfile?.name}</h2>
      <p>{userProfile?.email}</p>
      <button onClick={() => fetchProfile(userId)}>
        刷新
      </button>
    </div>
  );
};

# ChatGPT解释的设计模式示例：观察者模式
from abc import ABC, abstractmethod
from typing import List

class Observer(ABC):
    """观察者抽象基类"""
    
    @abstractmethod
    def update(self, subject: 'Subject') -> None:
        """当主题状态改变时被调用"""
        pass

class Subject(ABC):
    """主题抽象基类"""
    
    def __init__(self):
        self._observers: List[Observer] = []
    
    def attach(self, observer: Observer) -> None:
        """添加观察者"""
        if observer not in self._observers:
            self._observers.append(observer)
    
    def detach(self, observer: Observer) -> None:
        """移除观察者"""
        if observer in self._observers:
            self._observers.remove(observer)
    
    def notify(self) -> None:
        """通知所有观察者"""
        for observer in self._observers:
            observer.update(self)

# 具体实现
class StockPrice(Subject):
    """股票价格主题"""
    
    def __init__(self, symbol: str, price: float):
        super().__init__()
        self._symbol = symbol
        self._price = price
    
    @property
    def price(self) -> float:
        return self._price
    
    @price.setter
    def price(self, value: float) -> None:
        self._price = value
        self.notify()  # 价格变化时通知观察者
    
    @property
    def symbol(self) -> str:
        return self._symbol

class StockDisplay(Observer):
    """股票显示观察者"""
    
    def __init__(self, name: str):
        self._name = name
    
    def update(self, subject: StockPrice) -> None:
        print(f"{self._name}: {subject.symbol} 价格更新为 ${subject.price}")

# 使用示例
if __name__ == "__main__":
    # 创建股票价格主题
    apple_stock = StockPrice("AAPL", 150.0)
    
    # 创建观察者
    display1 = StockDisplay("交易终端1")
    display2 = StockDisplay("移动应用")
    
    # 注册观察者
    apple_stock.attach(display1)
    apple_stock.attach(display2)
    
    # 价格变化，自动通知所有观察者
    apple_stock.price = 155.0  # 输出: 交易终端1: AAPL 价格更新为 $155.0
                               #       移动应用: AAPL 价格更新为 $155.0

// Cursor辅助重构前的代码
class UserService {
  private users: any[] = [];
  
  getUserById(id: string) {
    for (let i = 0; i < this.users.length; i++) {
      if (this.users[i].id === id) {
        return this.users[i];
      }
    }
    return null;
  }
  
  getUsersByRole(role: string) {
    let result = [];
    for (let i = 0; i < this.users.length; i++) {
      if (this.users[i].role === role) {
        result.push(this.users[i]);
      }
    }
    return result;
  }
}

// Cursor建议重构后的代码
interface User {
  id: string;
  name: string;
  email: string;
  role: string;
  createdAt: Date;
}

class OptimizedUserService {
  private users: Map<string, User> = new Map();
  private roleIndex: Map<string, Set<string>> = new Map();
  
  /**
   * 添加用户并更新索引
   */
  addUser(user: User): void {
    this.users.set(user.id, user);
    
    // 更新角色索引
    if (!this.roleIndex.has(user.role)) {
      this.roleIndex.set(user.role, new Set());
    }
    this.roleIndex.get(user.role)!.add(user.id);
  }
  
  /**
   * 根据ID获取用户 - O(1)时间复杂度
   */
  getUserById(id: string): User | undefined {
    return this.users.get(id);
  }
  
  /**
   * 根据角色获取用户列表 - 优化的查询性能
   */
  getUsersByRole(role: string): User[] {
    const userIds = this.roleIndex.get(role);
    if (!userIds) return [];
    
    return Array.from(userIds)
      .map(id => this.users.get(id))
      .filter((user): user is User => user !== undefined);
  }
  
  /**
   * 批量操作支持
   */
  getUsersByIds(ids: string[]): User[] {
    return ids
      .map(id => this.users.get(id))
      .filter((user): user is User => user !== undefined);
  }
  
  /**
   * 获取用户统计信息
   */
  getUserStats(): { total: number; byRole: Record<string, number> } {
    const byRole: Record<string, number> = {};
    
    for (const [role, userIds] of this.roleIndex) {
      byRole[role] = userIds.size;
    }
    
    return {
      total: this.users.size,
      byRole
    };
  }
}

// Cursor生成的测试代码
import { describe, it, expect, beforeEach } from 'vitest';
import { OptimizedUserService } from './UserService';

describe('OptimizedUserService', () => {
  let userService: OptimizedUserService;
  
  const mockUsers = [
    {
      id: '1',
      name: 'Alice',
      email: 'alice@example.com',
      role: 'admin',
      createdAt: new Date('2023-01-01')
    },
    {
      id: '2',
      name: 'Bob',
      email: 'bob@example.com',
      role: 'user',
      createdAt: new Date('2023-01-02')
    },
    {
      id: '3',
      name: 'Charlie',
      email: 'charlie@example.com',
      role: 'user',
      createdAt: new Date('2023-01-03')
    }
  ];
  
  beforeEach(() => {
    userService = new OptimizedUserService();
    mockUsers.forEach(user => userService.addUser(user));
  });
  
  describe('getUserById', () => {
    it('should return user when valid id is provided', () => {
      const user = userService.getUserById('1');
      expect(user).toEqual(mockUsers[0]);
    });
    
    it('should return undefined when invalid id is provided', () => {
      const user = userService.getUserById('999');
      expect(user).toBeUndefined();
    });
  });
  
  describe('getUsersByRole', () => {
    it('should return users with specified role', () => {
      const users = userService.getUsersByRole('user');
      expect(users).toHaveLength(2);
      expect(users.map(u => u.name)).toEqual(['Bob', 'Charlie']);
    });
    
    it('should return empty array for non-existent role', () => {
      const users = userService.getUsersByRole('nonexistent');
      expect(users).toEqual([]);
    });
  });
  
  describe('getUserStats', () => {
    it('should return correct statistics', () => {
      const stats = userService.getUserStats();
      expect(stats.total).toBe(3);
      expect(stats.byRole).toEqual({
        admin: 1,
        user: 2
      });
    });
  });
  
  describe('performance tests', () => {
    it('should handle large datasets efficiently', () => {
      const largeUserService = new OptimizedUserService();
      
      // 添加大量用户
      const startTime = performance.now();
      for (let i = 0; i < 10000; i++) {
        largeUserService.addUser({
          id: `user_${i}`,
          name: `User ${i}`,
          email: `user${i}@example.com`,
          role: i % 3 === 0 ? 'admin' : 'user',
          createdAt: new Date()
        });
      }
      const addTime = performance.now() - startTime;
      
      // 测试查询性能
      const queryStart = performance.now();
      const user = largeUserService.getUserById('user_5000');
      const queryTime = performance.now() - queryStart;
      
      expect(user).toBeDefined();
      expect(addTime).toBeLessThan(1000); // 添加10k用户应在1秒内完成
      expect(queryTime).toBeLessThan(10);  // 单次查询应在10ms内完成
    });
  });
});

# AI工具协作的上下文管理系统
import json
from datetime import datetime
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, asdict
from enum import Enum

class AITool(Enum):
    CLAUDE = "claude"
    CHATGPT = "chatgpt"
    CURSOR = "cursor"

class TaskType(Enum):
    ARCHITECTURE = "architecture"
    CODING = "coding"
    DEBUGGING = "debugging"
    REVIEW = "review"
    DOCUMENTATION = "documentation"

@dataclass
class TaskContext:
    """任务上下文信息"""
    task_id: str
    task_type: TaskType
    description: str
    requirements: List[str]
    constraints: List[str]
    previous_results: List[Dict[str, Any]]
    created_at: datetime
    updated_at: datetime

@dataclass
class AIResponse:
    """AI工具响应结果"""
    tool: AITool
    task_id: str
    response_type: str
    content: str
    confidence: float
    suggestions: List[str]
    next_steps: List[str]
    timestamp: datetime

class AIWorkflowManager:
    """AI工具协作管理器"""
    
    def __init__(self):
        self.contexts: Dict[str, TaskContext] = {}
        self.responses: Dict[str, List[AIResponse]] = {}
        self.tool_capabilities = {
            AITool.CLAUDE: {
                'strengths': ['architecture', 'analysis', 'review'],
                'best_for': [TaskType.ARCHITECTURE, TaskType.REVIEW, TaskType.DOCUMENTATION]
            },
            AITool.CHATGPT: {
                'strengths': ['quick_response', 'debugging', 'explanation'],
                'best_for': [TaskType.DEBUGGING, TaskType.CODING]
            },
            AITool.CURSOR: {
                'strengths': ['real_time', 'completion', 'refactoring'],
                'best_for': [TaskType.CODING]
            }
        }
    
    def create_task(self, task_type: TaskType, description: str, 
                   requirements: List[str], constraints: List[str] = None) -> str:
        """创建新任务"""
        task_id = f"task_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
        
        context = TaskContext(
            task_id=task_id,
            task_type=task_type,
            description=description,
            requirements=requirements,
            constraints=constraints or [],
            previous_results=[],
            created_at=datetime.now(),
            updated_at=datetime.now()
        )
        
        self.contexts[task_id] = context
        self.responses[task_id] = []
        
        return task_id
    
    def select_optimal_tool(self, task_id: str) -> AITool:
        """根据任务类型选择最优工具"""
        context = self.contexts.get(task_id)
        if not context:
            return AITool.CHATGPT  # 默认选择
        
        # 根据任务类型和工具能力匹配
        for tool, capabilities in self.tool_capabilities.items():
            if context.task_type in capabilities['best_for']:
                return tool
        
        return AITool.CHATGPT
    
    def add_response(self, task_id: str, tool: AITool, response: Dict[str, Any]) -> None:
        """添加AI工具响应"""
        ai_response = AIResponse(
            tool=tool,
            task_id=task_id,
            response_type=response.get('type', 'general'),
            content=response.get('content', ''),
            confidence=response.get('confidence', 0.8),
            suggestions=response.get('suggestions', []),
            next_steps=response.get('next_steps', []),
            timestamp=datetime.now()
        )
        
        self.responses[task_id].append(ai_response)
        
        # 更新任务上下文
        if task_id in self.contexts:
            self.contexts[task_id].previous_results.append(asdict(ai_response))
            self.contexts[task_id].updated_at = datetime.now()
    
    def get_context_for_tool(self, task_id: str, target_tool: AITool) -> Dict[str, Any]:
        """为特定工具准备上下文信息"""
        context = self.contexts.get(task_id)
        responses = self.responses.get(task_id, [])
        
        if not context:
            return {}
        
        # 筛选相关的历史响应
        relevant_responses = []
        for response in responses:
            if response.tool != target_tool:  # 来自其他工具的响应
                relevant_responses.append({
                    'tool': response.tool.value,
                    'content': response.content,
                    'suggestions': response.suggestions
                })
        
        return {
            'task_description': context.description,
            'requirements': context.requirements,
            'constraints': context.constraints,
            'previous_insights': relevant_responses,
            'task_type': context.task_type.value
        }
    
    def generate_handoff_prompt(self, task_id: str, from_tool: AITool, 
                               to_tool: AITool) -> str:
        """生成工具交接提示"""
        context = self.get_context_for_tool(task_id, to_tool)
        
        prompt_templates = {
            (AITool.CLAUDE, AITool.CHATGPT): 
                "基于Claude的架构分析，请协助解决以下具体实现问题：",
            (AITool.CLAUDE, AITool.CURSOR):
                "根据Claude的设计方案，请在IDE中实现以下功能：",
            (AITool.CHATGPT, AITool.CLAUDE):
                "基于ChatGPT的问题分析，请进行深度的架构审查：",
            (AITool.CHATGPT, AITool.CURSOR):
                "根据ChatGPT的解决方案，请在编辑器中应用以下修改：",
            (AITool.CURSOR, AITool.CLAUDE):
                "基于Cursor的代码实现，请进行全面的代码审查：",
            (AITool.CURSOR, AITool.CHATGPT):
                "根据Cursor的编码结果，请解释以下技术细节："
        }
        
        template = prompt_templates.get((from_tool, to_tool), 
                                      "请基于之前的分析结果继续处理：")
        
        return f"{template}\n\n任务描述：{context['task_description']}\n" \
               f"需求：{', '.join(context['requirements'])}\n" \
               f"之前的分析结果：{json.dumps(context['previous_insights'], ensure_ascii=False, indent=2)}"

# 使用示例
workflow_manager = AIWorkflowManager()

# 创建架构设计任务
task_id = workflow_manager.create_task(
    task_type=TaskType.ARCHITECTURE,
    description="设计一个高并发的电商系统",
    requirements=[
        "支持10万并发用户",
        "99.9%可用性",
        "微服务架构",
        "支持水平扩展"
    ],
    constraints=[
        "预算限制在100万以内",
        "6个月内上线",
        "团队技术栈：Java/Spring Boot"
    ]
)

# 选择最优工具（Claude用于架构设计）
optimal_tool = workflow_manager.select_optimal_tool(task_id)
print(f"推荐使用工具：{optimal_tool.value}")

# 模拟Claude的响应
claude_response = {
    'type': 'architecture_design',
    'content': '基于微服务架构的电商系统设计方案...',
    'confidence': 0.9,
    'suggestions': [
        '使用Spring Cloud Gateway作为API网关',
        '采用Redis集群进行缓存',
        '使用MySQL主从复制'
    ],
    'next_steps': [
        '详细设计各微服务接口',
        '制定数据库分片策略',
        '设计监控和日志方案'
    ]
}

workflow_manager.add_response(task_id, AITool.CLAUDE, claude_response)

# 生成交接给ChatGPT的提示
handoff_prompt = workflow_manager.generate_handoff_prompt(
    task_id, AITool.CLAUDE, AITool.CHATGPT
)
print("交接提示：", handoff_prompt)

// 质量控制系统
interface QualityMetrics {
  codeQuality: number;
  testCoverage: number;
  performance: number;
  maintainability: number;
  security: number;
}

interface AIContribution {
  tool: string;
  contribution: string;
  confidence: number;
  humanReview: boolean;
  qualityScore: number;
}

class QualityController {
  private qualityThresholds = {
    codeQuality: 0.8,
    testCoverage: 0.85,
    performance: 0.9,
    maintainability: 0.8,
    security: 0.95
  };
  
  /**
   * 评估AI生成代码的质量
   */
  async evaluateAIContribution(
    code: string, 
    tool: string, 
    context: any
  ): Promise<AIContribution> {
    const metrics = await this.analyzeCode(code);
    const qualityScore = this.calculateQualityScore(metrics);
    
    return {
      tool,
      contribution: code,
      confidence: this.calculateConfidence(metrics, tool),
      humanReview: qualityScore < 0.8, // 低质量需要人工审查
      qualityScore
    };
  }
  
  private async analyzeCode(code: string): Promise<QualityMetrics> {
    // 集成多种代码分析工具
    const eslintResults = await this.runESLint(code);
    const testResults = await this.analyzeTestCoverage(code);
    const performanceResults = await this.analyzePerformance(code);
    
    return {
      codeQuality: eslintResults.score,
      testCoverage: testResults.coverage,
      performance: performanceResults.score,
      maintainability: this.calculateMaintainability(code),
      security: await this.securityAnalysis(code)
    };
  }
  
  private calculateQualityScore(metrics: QualityMetrics): number {
    const weights = {
      codeQuality: 0.25,
      testCoverage: 0.2,
      performance: 0.2,
      maintainability: 0.2,
      security: 0.15
    };
    
    return Object.entries(metrics).reduce((score, [key, value]) => {
      return score + (value * weights[key as keyof QualityMetrics]);
    }, 0);
  }
  
  /**
   * 生成改进建议
   */
  generateImprovementSuggestions(metrics: QualityMetrics): string[] {
    const suggestions: string[] = [];
    
    if (metrics.codeQuality < this.qualityThresholds.codeQuality) {
      suggestions.push("建议优化代码结构，减少复杂度");
    }
    
    if (metrics.testCoverage < this.qualityThresholds.testCoverage) {
      suggestions.push("需要增加测试用例，提高覆盖率");
    }
    
    if (metrics.performance < this.qualityThresholds.performance) {
      suggestions.push("存在性能问题，建议优化算法或数据结构");
    }
    
    return suggestions;
  }
}

# 技能提升追踪系统
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Dict, List
import matplotlib.pyplot as plt

@dataclass
class SkillMetric:
    skill_name: str
    proficiency_level: float  # 0-10
    learning_velocity: float  # 技能提升速度
    ai_assistance_ratio: float  # AI辅助比例
    practice_hours: int
    
class SkillTracker:
    def __init__(self):
        self.skills: Dict[str, List[SkillMetric]] = {}
        
    def track_skill_progress(self, skill_name: str, 
                           current_level: float,
                           ai_assistance: float,
                           hours_practiced: int) -> None:
        """追踪技能进步"""
        if skill_name not in self.skills:
            self.skills[skill_name] = []
            
        # 计算学习速度
        if len(self.skills[skill_name]) > 0:
            last_metric = self.skills[skill_name][-1]
            velocity = (current_level - last_metric.proficiency_level) / 30  # 假设30天间隔
        else:
            velocity = 0
            
        metric = SkillMetric(
            skill_name=skill_name,
            proficiency_level=current_level,
            learning_velocity=velocity,
            ai_assistance_ratio=ai_assistance,
            practice_hours=hours_practiced
        )
        
        self.skills[skill_name].append(metric)
    
    def analyze_ai_impact(self, skill_name: str) -> Dict[str, float]:
        """分析AI对技能学习的影响"""
        if skill_name not in self.skills:
            return {}
            
        metrics = self.skills[skill_name]
        
        # 分析AI辅助比例与学习速度的关系
        high_ai_metrics = [m for m in metrics if m.ai_assistance_ratio > 0.7]
        low_ai_metrics = [m for m in metrics if m.ai_assistance_ratio < 0.3]
        
        high_ai_velocity = sum(m.learning_velocity for m in high_ai_metrics) / len(high_ai_metrics) if high_ai_metrics else 0
        low_ai_velocity = sum(m.learning_velocity for m in low_ai_metrics) / len(low_ai_metrics) if low_ai_metrics else 0
        
        return {
            'high_ai_velocity': high_ai_velocity,
            'low_ai_velocity': low_ai_velocity,
            'velocity_improvement': (high_ai_velocity - low_ai_velocity) / low_ai_velocity * 100 if low_ai_velocity > 0 else 0,
            'current_level': metrics[-1].proficiency_level if metrics else 0
        }

# 实际使用示例
tracker = SkillTracker()

# 追踪React技能进步
react_progress = [
    (3.2, 0.2, 20),  # 初始水平，低AI辅助
    (4.1, 0.6, 35),  # 开始使用AI
    (5.8, 0.8, 40),  # 高AI辅助
    (7.2, 0.9, 45),  # 深度AI协作
    (8.5, 0.85, 50)  # 成熟应用
]

for i, (level, ai_ratio, hours) in enumerate(react_progress):
    tracker.track_skill_progress("React", level, ai_ratio, hours)

# 分析AI影响
react_analysis = tracker.analyze_ai_impact("React")
print(f"React技能分析：{react_analysis}")

# 追踪其他技能
skills_data = {
    "TypeScript": [(2.5, 0.1, 15), (4.2, 0.7, 30), (6.8, 0.9, 40), (8.1, 0.85, 45)],
    "Node.js": [(4.0, 0.3, 25), (5.5, 0.6, 35), (7.2, 0.8, 45), (8.7, 0.9, 50)],
    "Docker": [(1.8, 0.4, 10), (3.5, 0.8, 20), (5.9, 0.9, 30), (7.6, 0.85, 35)]
}

for skill, progress in skills_data.items():
    for level, ai_ratio, hours in progress:
        tracker.track_skill_progress(skill, level, ai_ratio, hours)

// AI工具使用陷阱检测与预防系统
interface AITrap {
  name: string;
  description: string;
  symptoms: string[];
  solutions: string[];
  prevention: string[];
}

class AITrapDetector {
  private commonTraps: AITrap[] = [
    {
      name: "过度依赖",
      description: "完全依赖AI生成的代码，失去独立思考能力",
      symptoms: [
        "不经思考直接使用AI生成的代码",
        "遇到问题首先求助AI而非自己分析",
        "对AI生成代码的原理不理解"
      ],
      solutions: [
        "设定AI使用比例上限（建议不超过70%）",
        "强制进行代码审查和理解",
        "定期进行无AI编程练习"
      ],
      prevention: [
        "建立代码审查流程",
        "保持学习和思考习惯",
        "定期评估技能水平"
      ]
    },
    {
      name: "上下文丢失",
      description: "AI工具之间缺乏有效的上下文传递",
      symptoms: [
        "重复解释相同问题",
        "AI给出不一致的建议",
        "工作流程断裂"
      ],
      solutions: [
        "建立统一的上下文管理系统",
        "使用标准化的信息传递格式",
        "定期同步各工具的理解状态"
      ],
      prevention: [
        "设计完整的工作流程",
        "建立信息共享机制",
        "使用项目文档作为共同参考"
      ]
    },
    {
      name: "质量控制缺失",
      description: "对AI生成内容缺乏有效的质量控制",
      symptoms: [
        "代码质量不稳定",
        "出现安全漏洞",
        "性能问题频发"
      ],
      solutions: [
        "建立多层质量检查机制",
        "集成自动化测试工具",
        "定期进行安全审计"
      ],
      prevention: [
        "制定代码质量标准",
        "建立持续集成流程",
        "培养质量意识"
      ]
    }
  ];
  
  /**
   * 检测当前工作流程中的潜在陷阱
   */
  detectTraps(workflowMetrics: any): string[] {
    const detectedTraps: string[] = [];
    
    // 检测过度依赖
    if (workflowMetrics.aiUsageRatio > 0.8) {
      detectedTraps.push("过度依赖");
    }
    
    // 检测上下文丢失
    if (workflowMetrics.contextConsistency < 0.7) {
      detectedTraps.push("上下文丢失");
    }
    
    // 检测质量控制缺失
    if (workflowMetrics.qualityScore < 0.8) {
      detectedTraps.push("质量控制缺失");
    }
    
    return detectedTraps;
  }
  
  /**
   * 获取陷阱的解决方案
   */
  getSolutions(trapName: string): string[] {
    const trap = this.commonTraps.find(t => t.name === trapName);
    return trap ? trap.solutions : [];
  }
}

// 使用示例
const trapDetector = new AITrapDetector();

const currentMetrics = {
  aiUsageRatio: 0.85,  // AI使用比例过高
  contextConsistency: 0.6,  // 上下文一致性较低
  qualityScore: 0.75  // 质量分数偏低
};

const detectedTraps = trapDetector.detectTraps(currentMetrics);
console.log("检测到的陷阱：", detectedTraps);

detectedTraps.forEach(trap => {
  const solutions = trapDetector.getSolutions(trap);
  console.log(`${trap}的解决方案：`, solutions);
});

# 持续优化系统
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Tuple

class WorkflowOptimizer:
    def __init__(self):
        self.metrics_history: List[Dict] = []
        self.optimization_rules = {
            'efficiency': self._optimize_efficiency,
            'quality': self._optimize_quality,
            'collaboration': self._optimize_collaboration
        }
    
    def collect_metrics(self, period_metrics: Dict) -> None:
        """收集周期性指标"""
        period_metrics['timestamp'] = datetime.now()
        self.metrics_history.append(period_metrics)
    
    def analyze_trends(self, metric_name: str, periods: int = 12) -> Dict:
        """分析指标趋势"""
        if len(self.metrics_history) < periods:
            return {'trend': 'insufficient_data'}
        
        recent_data = self.metrics_history[-periods:]
        values = [data.get(metric_name, 0) for data in recent_data]
        
        # 计算趋势
        x = np.arange(len(values))
        slope = np.polyfit(x, values, 1)[0]
        
        # 计算变异系数
        cv = np.std(values) / np.mean(values) if np.mean(values) > 0 else 0
        
        return {
            'trend': 'increasing' if slope > 0.01 else 'decreasing' if slope < -0.01 else 'stable',
            'slope': slope,
            'variability': 'high' if cv > 0.2 else 'medium' if cv > 0.1 else 'low',
            'current_value': values[-1],
            'average': np.mean(values)
        }
    
    def generate_optimization_recommendations(self) -> List[Dict]:
        """生成优化建议"""
        recommendations = []
        
        if len(self.metrics_history) < 3:
            return [{'type': 'info', 'message': '数据不足，需要更多历史数据'}]
        
        # 分析各项指标
        key_metrics = ['development_speed', 'code_quality', 'ai_effectiveness', 'collaboration_score']
        
        for metric in key_metrics:
            trend_analysis = self.analyze_trends(metric)
            
            if trend_analysis['trend'] == 'decreasing':
                recommendations.append({
                    'type': 'warning',
                    'metric': metric,
                    'message': f'{metric}呈下降趋势，需要关注',
                    'suggestions': self._get_improvement_suggestions(metric)
                })
            elif trend_analysis['variability'] == 'high':
                recommendations.append({
                    'type': 'attention',
                    'metric': metric,
                    'message': f'{metric}波动较大，需要稳定化',
                    'suggestions': self._get_stabilization_suggestions(metric)
                })
        
        return recommendations
    
    def _get_improvement_suggestions(self, metric: str) -> List[str]:
        """获取改进建议"""
        suggestions_map = {
            'development_speed': [
                '优化AI工具配置',
                '改进代码模板和片段',
                '加强工具间协作',
                '减少重复性工作'
            ],
            'code_quality': [
                '加强代码审查流程',
                '提升AI生成代码的质量标准',
                '增加自动化测试覆盖率',
                '定期进行重构'
            ],
            'ai_effectiveness': [
                '优化提示词工程',
                '改进上下文管理',
                '调整工具使用策略',
                '加强人工干预'
            ],
            'collaboration_score': [
                '改进信息传递机制',
                '统一工作流程标准',
                '加强团队培训',
                '优化工具集成'
            ]
        }
        
        return suggestions_map.get(metric, ['需要具体分析'])
    
    def _get_stabilization_suggestions(self, metric: str) -> List[str]:
        """获取稳定化建议"""
        return [
            '建立标准化流程',
            '减少外部干扰因素',
            '加强质量控制',
            '定期校准和调整'
        ]

# 使用示例
optimizer = WorkflowOptimizer()

# 模拟12个月的数据
monthly_data = [
    {'development_speed': 85, 'code_quality': 78, 'ai_effectiveness': 82, 'collaboration_score': 75},
    {'development_speed': 88, 'code_quality': 80, 'ai_effectiveness': 85, 'collaboration_score': 78},
    {'development_speed': 92, 'code_quality': 83, 'ai_effectiveness': 88, 'collaboration_score': 82},
    {'development_speed': 89, 'code_quality': 85, 'ai_effectiveness': 90, 'collaboration_score': 85},
    {'development_speed': 94, 'code_quality': 87, 'ai_effectiveness': 92, 'collaboration_score': 88},
    {'development_speed': 91, 'code_quality': 84, 'ai_effectiveness': 89, 'collaboration_score': 86},
    {'development_speed': 96, 'code_quality': 89, 'ai_effectiveness': 94, 'collaboration_score': 90},
    {'development_speed': 93, 'code_quality': 86, 'ai_effectiveness': 91, 'collaboration_score': 87},
    {'development_speed': 97, 'code_quality': 91, 'ai_effectiveness': 96, 'collaboration_score': 92},
    {'development_speed': 95, 'code_quality': 88, 'ai_effectiveness': 93, 'collaboration_score': 89},
    {'development_speed': 98, 'code_quality': 93, 'ai_effectiveness': 97, 'collaboration_score': 94},
    {'development_speed': 96, 'code_quality': 90, 'ai_effectiveness': 95, 'collaboration_score': 91}
]

for data in monthly_data:
    optimizer.collect_metrics(data)

# 生成优化建议
recommendations = optimizer.generate_optimization_recommendations()
for rec in recommendations:
    print(f"[{rec['type'].upper()}] {rec['message']}")
    if 'suggestions' in rec:
        for suggestion in rec['suggestions']:
            print(f"  - {suggestion}")