jd-analyzer

# JD Analyzer Skill ## 触发条件 当用户需要： - 分析职位描述（JD） - 提取关键技能和要求 - 评估简历与JD的匹配度 - 识别技能差距 - 为特定公司准备面试 ## 分析维度 ### 1. 基本信息 - 职位名称和级别 - 团队和部门 - 工作地点 - 职位类型（全职/兼职/实习） ### 2. 技能分析 #### 编程语言 - 必需语言（Required） - 优先语言（Preferred） - 框架和库 - 工具和平台 #### 系统设计 - 分布式系统 - 微服务架构 - 数据库设计 - 可扩展性要求 - 性能优化 #### 领域知识 - 行业特定知识 - 业务领域理解 - 专业术语 ### 3. 软技能 - 沟通能力 - 领导力 - 团队协作 - 问题解决 - 适应能力 ### 4. 资格要求 - 教育背景（学位、专业） - 工作经验年限 - 特定领域经验 - 认证或证书 ### 5. 职责范围 - 主要工作内容 - 项目类型 - 影响范围 - 决策权限 ### 6. 加分项 - 优先考虑的技能 - 额外的认证或经验 - 特殊贡献或成就 ## 工作流程 ### 步骤 1: 提取原始信息 从JD文本中提取： - 所有技能关键词 - 经验要求 - 教育要求 - 职责描述 ### 步骤 2: 结构化分析 将提取的信息分类到： ```json { "structured_analysis": { "required_skills": [...], "preferred_skills": [...], "responsibilities": [...], "minimum_qualifications": [...], "preferred_qualifications": [...] } } ``` ### 步骤 3: 技能匹配 对比用户基础简历（data/resume/base.json）： ```json { "skill_match_analysis": { "matched_skills": [ {"skill": "Python", "proficiency": "expert"} ], "partial_match_skills": [ {"skill": "Java", "proficiency": "intermediate", "required": "advanced"} ], "missing_skills": [ {"skill": "C++", "action": "需要学习基础"} ] } } ``` ### 步骤 4: 生成建议 基于匹配分析提供： - 需要补充的技能 - 应该突出的经验 - 面试准备重点 - 简历优化建议 ## 技能提取模式 ### 编程语言识别 识别以下模式： - "Proficiency in/with X" - "Experience with/using X" - "Strong knowledge of X" - "X developer" - "X, Y, or Z" ### 经验级别判断 - Entry: 0-2 years - Mid: 2-5 years - Senior: 5+ years - Staff/Principal: 8+ years ### 系统设计关键词 - "Distributed systems" - "Microservices architecture" - "Scalable systems" - "High-availability" - "Large-scale systems" - "Cloud-native" ### 软技能识别 - "Excellent communication" - "Leadership experience" - "Team player" - "Problem-solving skills" - "Self-motivated" ## 输出格式 ### 公司信息文件更新 更新 `data/companies/{company}.json` 的 `job_descriptions` 部分： ```json { "job_descriptions": { "pos_001": { "title": "Software Engineer III", "description_source": "...", "scraped_at": "2025-01-09T00:00:00Z", "raw_text": "完整JD文本...", "structured_analysis": { "required_skills": ["Python", "Distributed Systems"], "preferred_skills": ["Kubernetes", "ML"], "responsibilities": ["设计并开发软件"], "minimum_qualifications": ["计算机学位", "3年经验"], "preferred_qualifications": ["硕士", "5年经验"] }, "skill_match_analysis": { "matched_skills": [...], "partial_match_skills": [...], "missing_skills": [...] } } } } ``` ## 使用示例 ### 场景 1: 添加新公司时 用户提供JD文本，自动： 1. 提取所有技能和要求 2. 分析与用户简历的匹配度 3. 生成结构化分析报告 4. 保存到公司信息文件 ### 场景 2: 简历优化前 为简历优化器提供： - 关键技能列表 - 优先级排序 - 匹配度评分 - 优化建议 ### 场景 3: 面试准备时 生成面试准备重点： - 技术主题清单 - 可能的问题类型 - 需要深入研究的领域 ## 匹配规则 ### 完全匹配 - 用户有直接使用经验 - 项目中实际应用 - 可以深度讨论 ### 部分匹配 - 了解相关技术 - 有相似技术经验 - 可以快速学习 ### 缺失 - 完全没有经验 - 需要从头学习 - 建议学习路径 ## 权重计算 用于匹配度评分： - 必需技能：权重 2.0 - 优先技能：权重 1.0 - 加分项：权重 0.5 ``` 匹配度 = Σ(匹配等级 × 权重) / Σ(最大权重) ``` ## 注意事项 1. **上下文理解**: 考虑JD的整体语境，而不是孤立的关键词 2. **技能归类**: 将不同名称但本质相同的技能归类 3. **公司特色**: 识别公司的技术文化（如Google重视可扩展性） 4. **隐含要求**: 识别JD中未明确表达的期望 5. **动态更新**: 随着用户技能提升，重新评估匹配度 ## 高级分析功能（Phase 2新增） ### 公司文化分析 #### 文化指标识别 **工作文化关键词**: ```json { "culture_indicators": { "pace": { "fast": ["fast-paced", "move fast", "rapid iteration", "agile", "startup environment"], "balanced": ["work-life balance", "sustainable pace", "flexible hours"], "intense": ["high-pressure", "demanding", "deadline-driven"] }, "collaboration": { "collaborative": ["team player", "cross-functional", "collaborate", "pair programming"], "independent": ["self-starter", "independent", "autonomous", "own initiatives"], "leadership": ["lead", "mentor", "guide", "influence", "drive decisions"] }, "innovation": { "innovative": ["innovative", "cutting-edge", "pioneer", "breakthrough", "novel"], "stable": ["stable", "proven", "established", "reliable", "mature"], "scalable": ["scale", "growth", "expand", "multiply", "massive impact"] }, "values": { "customer_obsessed": ["customer obsessed", "customer first", "user-centric"], "data_driven": ["data-driven", "analytics", "metrics", "experimentation"], "ownership": ["ownership", "end-to-end", "responsibility", "accountability"], "excellence": ["high standards", "excellence", "quality", "best practices"] } } } ``` #### 文化匹配分析 ```json { "culture_analysis": { "company_culture_type": "Innovative & Fast-paced", "key_culture_traits": [ "Data-driven decision making", "Fast iteration and experimentation", "Collaborative cross-functional work", "High ownership and accountability" ], "culture_indicators_found": [ {"trait": "fast-paced", "evidence": "fast-paced environment", "frequency": 3}, {"trait": "data-driven", "evidence": "data-driven approach", "frequency": 5}, {"trait": "collaborative", "evidence": "cross-functional teams", "frequency": 2} ], "fit_assessment": { "score": 85, "strengths": ["匹配快速迭代环境", "有跨团队协作经验"], "considerations": ["需要适应高压环境", "强调数据驱动决策"] } } } ``` ### 薪资范围检测 #### 薪资信息提取 **识别模式**: ```regex # 基础薪资 "(\$?\d{2,3}k?[-–to]\$?\d{2,3}k?)\s*(per year|annually|/year|annual)" # 股票/股权 "(stock|equity|RSU|option|grant).*?(\$?\d{2,3}k?)" # 奖金 "(bonus|signing|target).*?(\$?\d{1,2}k?|\d{1,2}%)" # 总薪酬 "(total compensation|TC|OTE).*?(\$?\d{2,3}k?)" ``` **薪资结构分析**: ```json { "salary_analysis": { "base_salary": { "range": "$150,000 - $200,000", "currency": "USD", "period": "yearly", "confidence": "high" }, "equity": { "mentioned": true, "estimated_range": "$50,000 - $100,000/year", "type": "RSU", "vesting": "4 years" }, "bonus": { "mentioned": true, "percentage": "20%", "estimated_value": "$30,000 - $40,000" }, "total_compensation": { "estimated_range": "$230,000 - $340,000", "breakdown": ["Base: $150k-$200k", "Bonus: $30k-$40k", "Equity: $50k-$100k"] }, "market_comparison": { "percentile": "75th", "comparison": "Above market average" } } } ``` #### 地区薪资调整 ```json { "location_adjustments": { "SF Bay Area": 1.0, "New York": 0.95, "Seattle": 0.90, "Austin": 0.80, "Remote (US)": 0.85, "Beijing": 0.60, "Shanghai": 0.62 } } ``` ### 增强的技能提取模式 #### AI/ML技能识别 ```json { "ai_ml_skills": { "machine_learning": ["Machine Learning", "ML", "Deep Learning", "Neural Networks"], "frameworks": ["TensorFlow", "PyTorch", "Keras", "Scikit-learn", "MXNet"], "nlp": ["NLP", "Natural Language Processing", "Transformers", "BERT", "GPT"], "computer_vision": ["Computer Vision", "CV", "Image Processing", "CNN"], "mlops": ["MLOps", "ML Engineering", "Model Deployment", "Feature Engineering"] } } ``` #### 云平台技能 ```json { "cloud_skills": { "aws": ["AWS", "Amazon Web Services", "EC2", "S3", "Lambda", "RDS", "DynamoDB"], "gcp": ["GCP", "Google Cloud", "GKE", "BigQuery", "Cloud Functions"], "azure": ["Azure", "Microsoft Azure", "AKS", "Azure Functions"], "cloud_native": ["Kubernetes", "K8s", "Docker", "Helm", "Istio", "Service Mesh"] } } ``` #### 数据技能