linkedin-job-analyzer/
├── exportadores/              # Export subsystem (Factory Pattern)
│   ├── base_exporter.py       # Abstract base class for exporters
│   ├── json_exporter.py       # JSON export implementation
│   ├── excel_exporter.py      # Excel export implementation
│   └── exporter_factory.py    # Factory for creating exporters
│
├── inteligencia_artificial/   # AI integration subsystem
│   └── gpt_analyzer.py        # OpenAI GPT integration
│
├── logica_negocio/            # Business logic layer
│   └── servicio_vacantes.py   # JobService facade
│
├── scraping/                  # Web scraping subsystem (Strategy Pattern)
│   ├── base_scraper.py        # Abstract scraper interface
│   └── linkedin_scraper.py    # LinkedIn-specific implementation
│
├── static/                    # Frontend assets
│   ├── script.js              # Client-side JavaScript
│   └── style.css              # Styles
│
├── templates/                 # HTML templates
│   └── index.html             # Main search interface
│
├── utilidades/                # Utility functions
│   └── text_cleaner.py        # Text processing utilities
│
├── datos_extraidos/           # Output directory (generated at runtime)
│   ├── linkedin_YYYYMMDD_HHMMSS.json
│   └── linkedin_YYYYMMDD_HHMMSS.xlsx
│
├── flask_app.py               # Application entry point
├── requirements.txt           # Python dependencies
├── .env.example               # Environment variable template
├── .gitignore                 # Git ignore rules
├── LICENSE                    # Project license
└── README.md                  # Project documentation

class DataExporter(ABC):
    @abstractmethod
    def exportar(self, datos: Dict, ruta_salida: str) -> str:
        pass

1. **Objetivo del Rol**: ...
2. **Stack Tecnológico Principal**: ...
3. **Skills Blandas**: ...
4. **Nivel de Experiencia**: ...

procesar_busqueda(termino_busqueda: str) -> Dict
    # Orchestrates: scraping → cleaning → packaging

generar_resumen_ia(titulo: str, habilidades: List[str]) -> str
    # Delegates to AIAnalyzer

guardar_datos(datos: Dict, formato: str) -> List[str]
    # Uses ExporterFactory to save files

class ScraperStrategy(ABC):
    @abstractmethod
    def extraer_datos(self, termino_busqueda: str) -> Dict:
        pass

{
    'exito': True,
    'titulo_oferta': str,
    'url': str,
    'habilidades_brutas': List[str]  # Uncleaned text
}

['click', 'show', 'more', 'less', 'see', 'view', 
 'apply', 'job', 'description', 'ver', 'más']

scraper = LinkedInScraper()
servicio = JobService(scraper=scraper)
app = Flask(__name__)

flask
selenium
beautifulsoup4
webdriver-manager
pandas
openpyxl
openai
python-dotenv

OPENAI_API_KEY=sk-your-key-here

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

Presentation (flask_app.py, templates/, static/)
      ↓
Business Logic (logica_negocio/)
      ↓
Services (scraping/, inteligencia_artificial/, exportadores/)
      ↓
Utilities (utilidades/)

# scraping/indeed_scraper.py
from .base_scraper import ScraperStrategy

class IndeedScraper(ScraperStrategy):
    def extraer_datos(self, termino_busqueda: str) -> Dict:
        # Implementation
        pass

# flask_app.py
from scraping.indeed_scraper import IndeedScraper

scraper = IndeedScraper()  # Change this line
servicio = JobService(scraper=scraper)

# exportadores/csv_exporter.py
from .base_exporter import DataExporter
import csv

class CSVExporter(DataExporter):
    def exportar(self, datos: Dict, ruta_salida: str) -> str:
        # Implementation
        return ruta_salida

# exportadores/exporter_factory.py
from .csv_exporter import CSVExporter

class ExporterFactory:
    @staticmethod
    def obtener_exportador(formato: str):
        if formato == 'json':
            return JSONExporter()
        elif formato == 'excel':
            return ExcelExporter()
        elif formato == 'csv':  # Add this
            return CSVExporter()

# utilidades/text_cleaner.py
class TextCleaner:
    @staticmethod
    def limpiar_habilidades(habilidades: List[str]) -> List[str]:
        # Existing method
        pass
    
    @staticmethod
    def extraer_emails(texto: str) -> List[str]:  # New method
        import re
        return re.findall(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', texto)

# utilidades/date_formatter.py
from datetime import datetime

class DateFormatter:
    @staticmethod
    def formato_latino(fecha: datetime) -> str:
        return fecha.strftime("%d/%m/%Y %H:%M")

# flask_app.py
@app.route('/estadisticas', methods=['GET'])
def estadisticas():
    # Get statistics from stored files
    return jsonify({"total_busquedas": 42})

# Standard library imports
import os
from datetime import datetime
from typing import Dict, List

# Third-party imports
from flask import Flask, request
import pandas as pd

# Local imports
from logica_negocio.servicio_vacantes import JobService
from scraping.linkedin_scraper import LinkedInScraper

# Patrón Fachada: Orquesta el flujo entre componentes
class JobService:
    pass

tests/
├── test_scraping/
│   ├── test_linkedin_scraper.py
│   └── test_scraper_strategy.py
├── test_exportadores/
│   ├── test_json_exporter.py
│   ├── test_excel_exporter.py
│   └── test_exporter_factory.py
├── test_logica_negocio/
│   └── test_servicio_vacantes.py
├── test_utilidades/
│   └── test_text_cleaner.py
└── fixtures/
    ├── sample_html.html
    └── sample_job_data.json