LinkedIn_AIHawk

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Table of Contents

1. Introduction
2. Features
3. Installation
4. Configuration
5. Usage
6. Documentation
7. Troubleshooting
8. Conclusion
9. Contributors
10. Credits
11. License
12. Disclaimer

Introduction

LinkedIn_AIHawk is a cutting-edge, automated tool designed to revolutionize the job search and application process on LinkedIn. In today's fiercely competitive job market, where opportunities can vanish in the blink of an eye, this program offers job seekers a significant advantage. By leveraging the power of automation and artificial intelligence, LinkedIn_AIHawk enables users to apply to a vast number of relevant positions efficiently and in a personalized manner, maximizing their chances of landing their dream job.

The Challenge of Modern Job Hunting

In the digital age, the job search landscape has undergone a dramatic transformation. While online platforms like LinkedIn have opened up a world of opportunities, they have also intensified competition. Job seekers often find themselves spending countless hours scrolling through listings, tailoring applications, and repetitively filling out forms. This process can be not only time-consuming but also emotionally draining, leading to job search fatigue and missed opportunities.

Enter LinkedIn_AIHawk: Your Personal Job Search Assistant

LinkedIn_AIHawk steps in as a game-changing solution to these challenges. It's not just a tool; it's your tireless, 24/7 job search partner. By automating the most time-consuming aspects of the job search process, it allows you to focus on what truly matters - preparing for interviews and developing your professional skills.

Features

1. Intelligent Job Search Automation

   • Customizable search criteria
   • Continuous scanning for new openings
   • Smart filtering to exclude irrelevant listings

2. Rapid and Efficient Application Submission

   • One-click applications using LinkedIn's "Easy Apply" feature
   • Form auto-fill using your profile information
   • Automatic document attachment (resume, cover letter)

3. AI-Powered Personalization

   • Dynamic response generation for employer-specific questions
   • Tone and style matching to fit company culture
   • Keyword optimization for improved application relevance

4. Volume Management with Quality

   • Bulk application capability
   • Quality control measures
   • Detailed application tracking

5. Intelligent Filtering and Blacklisting

   • Company blacklist to avoid unwanted employers
   • Title filtering to focus on relevant positions

6. Dynamic Resume Generation

   • Automatically creates tailored resumes for each application
   • Customizes resume content based on job requirements

7. Secure Data Handling

   • Manages sensitive information securely using YAML files

Installation

1. Download and Install Python:

   Ensure you have Python installed. If not, download and install it from Python's official website. For detailed instructions, refer to the tutorials:

   • How to Install Python on Windows
   • How to Install Python on Linux
   • How to Download and Install Python on macOS

2. Download and Install Google Chrome:

   • Download and install the latest version of Google Chrome in its default location from the official website.

3. Clone the repository:

   git clone https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application
   cd LinkedIn_AIHawk_automatic_job_application

4. Install the required packages:

   pip install -r requirements.txt

Configuration

1. secrets.yaml

This file contains sensitive information. Never share or commit this file to version control.

• email: [Your LinkedIn email]
  • Replace with your LinkedIn account email address
• password: [Your LinkedIn password]
  • Replace with your LinkedIn account password
• openai_api_key: [Your OpenAI API key]
  • Replace with your OpenAI API key for GPT integration
  • To obtain an API key, follow the tutorial at: https://medium.com/@lorenzozar/how-to-get-your-own-openai-api-key-f4d44e60c327
  • Note: You need to add credit to your OpenAI account to use the API. You can add credit by visiting the OpenAI billing dashboard.

2. config.yaml

This file defines your job search parameters and bot behavior. Each section contains options that you can customize:

• remote: [true/false]

  • Set to true to include remote jobs, false to exclude them

• experienceLevel:

  • Set desired experience levels to true, others to false

• jobTypes:

  • Set desired job types to true, others to false

• date:

  • Choose one time range for job postings by setting it to true, others to false

• positions:

  • List job titles you're interested in, one per line
  • Example:

    positions:
      - Software Developer
      - Data Scientist

• locations:

  • List locations you want to search in, one per line
  • Example:

    locations:
      - Italy
      - London

• distance: [number]

  • Set the radius for your job search in miles
  • Example: distance: 50

• companyBlacklist:

  • List companies you want to exclude from your search, one per line
  • Example:

    companyBlacklist:
      - Company X
      - Company Y

• titleBlacklist:

  • List keywords in job titles you want to avoid, one per line
  • Example:

    titleBlacklist:
      - Sales
      - Marketing

3. plain_text_resume.yaml

This file contains your resume information in a structured format. Fill it out with your personal details, education, work experience, and skills. This information is used to auto-fill application forms and generate customized resumes.

Each section has specific fields to fill out:

• personal_information:

  • Contains basic personal details
  • Example: name: "John Doe"

• self_identification:

  • Optional demographic information
  • Example: gender: "Male"

• legal_authorization:

  • Work authorization status
  • Use true or false for each field
  • Example: usWorkAuthorization: true

• work_preferences:

  • Your work-related preferences
  • Use true or false for each field
  • Example: remoteWork: true

• education_details:

  • List your educational background
  • Include degree, university, GPA, graduation year, field of study, and skills acquired
  • Example:

    - degree: "Bachelor's"
      university: "University of Example"
      gpa: "3.8"
      graduationYear: "2022"
      fieldOfStudy: "Computer Science"
      skillsAcquired:
        problemSolving: "4"

• experience_details:

  • List your work experiences
  • Include position, company, employment period, location, industry, key responsibilities, and skills acquired
  • Example:

    - position: "Software Developer"
      company: "Tech Corp"
      employmentPeriod: "Jan 2020 - Present"
      location: "San Francisco, USA"
      industry: "Technology"
      keyResponsibilities:
        responsibility1: "Developed web applications using React"
      skillsAcquired:
        adaptability: "3"

• Other sections like projects, availability, salary_expectations, certifications, skills, languages, and interests follow a similar format, with each item on a new line.

PLUS. data_folder_example

The data_folder_example folder contains a working example of how the files necessary for the bot's operation should be structured and filled out. This folder serves as a practical reference to help you correctly set up your work environment for the LinkedIn job search bot.

Contents

Inside this folder, you'll find example versions of the key files:

• secrets.yaml
• config.yaml
• plain_text_resume.yaml

These files are already populated with fictitious but realistic data. They show you the correct format and type of information to enter in each file.

Usage

Using this folder as a guide can be particularly helpful for:

1. Understanding the correct structure of each configuration file
2. Seeing examples of valid data for each field
3. Having a reference point while filling out your personal files

Important Note

Usage

0. LinkedIn language To ensure the bot works, your LinkedIn language must be set to English.

1. Data Folder: Ensure that your data_folder contains the following files:

   • secrets.yaml
   • config.yaml
   • plain_text_resume.yaml

2. Run the Bot:

   LinkedIn_AIHawk offers flexibility in how it handles your pdf resume:

• Dynamic Resume Generation: If you don't use the --resume option, the bot will automatically generate a unique resume for each application. This feature uses the information from your plain_text_resume.yaml file and tailors it to each specific job application, potentially increasing your chances of success by customizing your resume for each position.

  python main.py

• Using a Specific Resume: If you want to use a specific PDF resume for all applications, run the bot with the --resume option:

  python main.py --resume /path/to/your/resume.pdf

Documentation

For detailed information on each component and their respective roles, please refer to the Documentation file.

Troubleshooting

• ChromeDriver Issues: Ensure ChromeDriver is compatible with your installed Chrome version.

• Missing Files: Verify that all necessary files are present in the data folder.

• Invalid YAML: Check your YAML files for syntax errors.

  If you encounter any issues, you can open an issue on GitHub. I'll be more than happy to assist you!

Conclusion

LinkedIn_AIHawk provides a significant advantage in the modern job market by automating and enhancing the job application process. With features like dynamic resume generation and AI-powered personalization, it offers unparalleled flexibility and efficiency. Whether you're a job seeker aiming to maximize your chances of landing a job, a recruiter looking to streamline application submissions, or a career advisor seeking to offer better services, LinkedIn_AIHawk is an invaluable resource. By leveraging cutting-edge automation and artificial intelligence, this tool not only saves time but also significantly increases the effectiveness and quality of job applications in today's competitive landscape.

Contributors

• feder-cr - Creator and Maintainer

LinkedIn_AIHawk is still in beta, and your feedback, suggestions, and contributions are highly valued. Feel free to open issues, suggest enhancements, or submit pull requests to help improve the project. Let's work together to make LinkedIn_AIHawk an even more powerful tool for job seekers worldwide.

Credits

casual-markdown

• Description: This project uses the casual-markdown library, a lightweight regex-based Markdown parser with Table of Contents (TOC) support.
• Author: casualwriter
• Repository: casual-markdown

License

This project is licensed under the MIT License - see the LICENSE file for details.

Disclaimer

LinkedIn_AIHawk is developed for educational purposes only. The creator does not assume any responsibility for its use. Users should ensure they comply with LinkedIn's terms of service, any applicable laws and regulations, and ethical considerations when using this tool. The use of automated tools for job applications may have implications on user accounts, and caution is advised.