job-posting-analysis（职位发布分析）

Job Posting Analysis Skill

Purpose

Extract technology stack information from job postings and career pages, which often reveal internal tech stack details.

Operations

1. find_careers_page

Locate company's career/jobs page.

Search Strategies:

1. Common paths: /careers, /jobs, /work-with-us, /join-us
2. Subdomains: careers.{domain}, jobs.{domain}
3. Web search: site:{domain} careers OR jobs
4. Footer links on main site

Common Career Page URLs:

https://{domain}/careers
https://{domain}/jobs
https://careers.{domain}
https://jobs.{domain}
https://{domain}/about/careers
https://{domain}/company/careers

2. detect_ats_platform

Identify Applicant Tracking System in use.

ATS Detection Patterns:

{
  "Greenhouse": {
    "url_pattern": "boards.greenhouse.io",
    "indicates": ["Tech-forward startup", "Modern hiring"],
    "confidence": 95
  },
  "Lever": {
    "url_pattern": "jobs.lever.co",
    "indicates": ["Tech-forward startup", "Growth stage"],
    "confidence": 95
  },
  "Workday": {
    "url_pattern": ".wd5.myworkdayjobs.com|.wd3.myworkdayjobs.com",
    "indicates": ["Enterprise company", "Large org"],
    "confidence": 95
  },
  "Ashby": {
    "url_pattern": "jobs.ashbyhq.com",
    "indicates": ["Modern startup", "Tech-forward"],
    "confidence": 95
  },
  "iCIMS": {
    "url_pattern": "careers-.*\\.icims\\.com|icims.com",
    "indicates": ["Enterprise hiring"],
    "confidence": 95
  },
  "Taleo": {
    "url_pattern": "taleo.net",
    "indicates": ["Enterprise (Oracle)", "Large org"],
    "confidence": 95
  },
  "SmartRecruiters": {
    "url_pattern": "jobs.smartrecruiters.com",
    "indicates": ["Mid-market to Enterprise"],
    "confidence": 95
  },
  "BambooHR": {
    "url_pattern": ".bamboohr.com/jobs",
    "indicates": ["SMB company"],
    "confidence": 95
  },
  "Jobvite": {
    "url_pattern": "jobs.jobvite.com",
    "indicates": ["Mid-market hiring"],
    "confidence": 95
  },
  "Breezy HR": {
    "url_pattern": ".breezy.hr",
    "indicates": ["SMB startup"],
    "confidence": 95
  }
}

3. extract_tech_requirements

Parse job descriptions for technology mentions.

Extraction Patterns:

Experience with ([\w\s,/]+)
Proficiency in ([\w\s,/]+)
Knowledge of ([\w\s,/]+)
Tech stack:? ([\w\s,/]+)
Working knowledge of ([\w\s,/]+)
Familiar with ([\w\s,/]+)
Strong background in ([\w\s,/]+)
Required:?\s*([\w\s,/]+)
Nice to have:?\s*([\w\s,/]+)
Technologies:?\s*([\w\s,/]+)
Tools:?\s*([\w\s,/]+)

Technology Keyword Categories:

Languages:

JavaScript, TypeScript, Python, Java, Go, Rust, Ruby, PHP,
C#, C++, Kotlin, Swift, Scala, Elixir, Clojure

Frontend Frameworks:

React, Vue, Angular, Svelte, Next.js, Nuxt, Gatsby,
Redux, MobX, Zustand, React Query, Tailwind, Bootstrap

Backend Frameworks:

Node.js, Express, NestJS, Django, Flask, FastAPI,
Rails, Spring, .NET, Laravel, Phoenix

Databases:

PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch,
DynamoDB, Cassandra, Neo4j, Snowflake, BigQuery

Cloud/Infrastructure:

AWS, GCP, Azure, Kubernetes, Docker, Terraform,
Ansible, CloudFormation, Pulumi

Tools:

Git, GitHub, GitLab, Jenkins, CircleCI, GitHub Actions,
Datadog, New Relic, Grafana, Prometheus, Sentry

4. calculate_tech_frequency

Weight technologies by mention frequency across postings.

Scoring:

def calculate_frequency_score(tech, postings):
    mentions = sum(1 for p in postings if tech in p.requirements)
    total_postings = len(postings)

    frequency = mentions / total_postings

    # Classify importance
    if frequency >= 0.5:
        importance = "Core Stack"  # 50%+ of postings
    elif frequency >= 0.25:
        importance = "Common"      # 25-50%
    else:
        importance = "Occasional"  # < 25%

    return {
        "mentions": mentions,
        "frequency": frequency,
        "importance": importance
    }

5. analyze_role_patterns

Identify tech stack from role types.

Role Type Signals:

{
  "Frontend Engineer": {
    "implies": ["React/Vue/Angular", "JavaScript/TypeScript", "CSS frameworks"],
    "confidence": 70
  },
  "Backend Engineer": {
    "implies": ["Server-side language", "Database", "API development"],
    "confidence": 70
  },
  "Full Stack Engineer": {
    "implies": ["Frontend framework", "Backend framework", "Database"],
    "confidence": 65
  },
  "DevOps Engineer": {
    "implies": ["Cloud platform", "CI/CD", "Kubernetes/Docker", "IaC"],
    "confidence": 75
  },
  "Data Engineer": {
    "implies": ["Python/Scala", "Spark/Airflow", "Data warehouse"],
    "confidence": 75
  },
  "ML Engineer": {
    "implies": ["Python", "TensorFlow/PyTorch", "Cloud ML services"],
    "confidence": 75
  },
  "iOS Developer": {
    "implies": ["Swift", "Xcode", "iOS SDK"],
    "confidence": 85
  },
  "Android Developer": {
    "implies": ["Kotlin/Java", "Android SDK"],
    "confidence": 85
  }
}

Output

{
  "skill": "job_posting_analysis",
  "domain": "string",
  "results": {
    "careers_page": {
      "url": "string",
      "ats_platform": "Greenhouse",
      "ats_confidence": 95
    },
    "postings_analyzed": "number",
    "technologies_extracted": [
      {
        "name": "React",
        "category": "Frontend Framework",
        "mentions": 15,
        "total_postings": 20,
        "frequency": 0.75,
        "importance": "Core Stack",
        "contexts": [
          "Experience with React and TypeScript",
          "Build UIs using React"
        ],
        "confidence": 80
      }
    ],
    "role_distribution": {
      "Frontend": 5,
      "Backend": 8,
      "Full Stack": 4,
      "DevOps": 2,
      "Data": 1
    },
    "tech_stack_inference": {
      "frontend": ["React", "TypeScript", "Tailwind"],
      "backend": ["Node.js", "PostgreSQL", "Redis"],
      "infrastructure": ["AWS", "Kubernetes"],
      "confidence": "Medium"
    },
    "company_signals": {
      "engineering_size": "Large (20+ open roles)",
      "growth_stage": "Scaling",
      "tech_culture": "Modern (tech-forward ATS, current stack)"
    }
  },
  "evidence": [
    {
      "type": "job_posting",
      "title": "Senior Frontend Engineer",
      "url": "string",
      "technologies_mentioned": ["React", "TypeScript", "GraphQL"],
      "timestamp": "ISO-8601"
    }
  ]
}

Rate Limiting

• Careers page fetch: 10/minute
• Job posting pages: 20/minute
• ATS APIs: Varies by platform

Error Handling

• 404: No careers page found
• Access denied: ATS may require authentication
• Continue with partial data
• Fall back to search engine results

Security Considerations

• Only access public job postings
• Do not apply to jobs or create accounts
• Respect robots.txt
• Do not scrape PII (recruiter names, emails)
• Log all fetches for audit

Confidence Notes

Job posting data provides indirect signals:

• Technologies mentioned in job posts may not be currently deployed
• "Nice to have" vs "Required" distinction matters
• Combine with direct technical evidence for validation
• Base confidence: 60-80% (lower than direct signals)