Important

I no longer recommend using this bot. While it was helpful in the past, resume optimization is a significantly more effective strategy.

Why? Although this bot proved successful in my job search, what created the most impact was actually having an ATS-friendly PDF. Before optimizing my resume, this bot resulted in very little "conversions" (less than 1% of applications resulted in an interview). By sheer amount of applications, that was still a lot of interviews, but I missed many opportunities where I fit the offer perfectly.

My learnings: If you fit the job offer and your resume clearly reflects it, you will get interviews. It's that simple. But that usually means a tailored resume for each job, which is a lot of work (plus all the ATS formatting madness). Otherwise, you only hit interviews when your resume happens to match the job description by chance, like a broken clock being right twice a day.

The Solution: This realization took me down a rabbit hole over the last two years. I started by helping friends with manual resume optimization, then added AI to the mix, and eventually built a lot of automation. I reached a point where I built a tool that takes your resume and a job description and gives you a tailored resume for that job, no other work needed. Yes, it fulfils the ATS requirements as well.

Open Beta: The project grew from helping friends to helping friends-of-friends via a simple web app. Now, I'm opening it to everyone. It's completely free (I'm covering the LLM costs myself) because I want to see if I can help others with their job search, in the future if it grows I might charge for it, but right now I have no plans to as long as I can cover the costs myself without being a burden.

You can join the beta at beta.theresume.monster. There is a waiting list to keep costs under control, but I will invite you as soon as possible (check your spam folder).

Feedback: I'm open to feedback, as I say, I want to help, and it means learning and improving.

Legacy Support for LinkedIn GPT: I'm going to merge all open branches and fixes for this bot in case you still want to use it, but I highly discourage it. Use the resume optimizer instead, it's way more effective, requires no setup, and carries no risk of being banned from LinkedIn.

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LinkedIn GPT

Automatically apply to LinkedIn Easy Apply jobs. This bot answers the application questions as well!

This is a fork of a fork of the original LinkedIn Easy Apply Bot, but it is a very special fork of a fork, this one relies on LLMs to answer the questions.

This is for educational purposes only. I am not responsible if your LinkedIn account gets suspended or for anything else.

This bot is written in Python using Selenium and OpenAI.

Fork Notes

The original bot implementation, couldn't handle open questions, just used keywords and predefined answers. Such couldn't complete a lot of the applications, as any open question or weird selector would make the bot unable to answer. Now that we have LLM, this is an easy problem to solve, just ask the bot to answer the question, and it will do it.

Another great benefit, is that you can provide way more information to the bot, so it can address truthfully the job requirements, and the questions, just as you would do.

I did try to tidy the code a bit, but I didn't want to spend too much time on it, as I just wanted to get it working, so there is still a lot of work to do there.

Thank you for everyone that contributed to the original bot, and all the forks, made my work way easier.

by Jorge Frías

Future updates

• I will keep updating this fork as I use it for my own "educational research".
• I will add features as I find fun applications, or I require them for my "educational research".

Setup

OpenAI API Key

First you need to provide your Open AI API key using environment variable OPEN_AI_API_KEY.

#copy example env file to env
cp .env.example .env

#.env
OPEN_AI_API_KEY="PASTE OPEN API KEY"

I recommend to set a Rate Limit on your OpenAI account if you plan to leave the bot running for a long time, as it can get expensive quickly. I tried to use the cheapest models possible, but still requires GPT-3.5-Turbo to work.

Your information

Your information is provided with a directory containing the following files:

• config.yaml. This file contains the information used to search on LinkedIn and fill in your personal information. Most of this is self-explanatory but if you need explanations please see the end of this README.

• plain_text_resume.md. Will be used to answer the questions, it's provided in MarkDown format.

• plain_text_cover_letter.md. Will be used when the form ask for a cover letter. When the form ask to write a cover letter (not upload it), the bot will adjust the cover letter to the job description.

  • You can use placeholders in your cover letter, a placeholder is defined as [[placeholder]], the LLM will look onto the job description to fill in the placeholders. E.g. [[company]] will be replaced by the given company name.

• personal_data.md. More information about you, what you want of the job search, work authorization, extended information not covered by the resume, etc. This will be used to answer the questions, and inform other parts of the application. This file doesn't have any structure, will be interpreted by the LLM so fell free to add structure or information as you see fit.

• job-filters.md. This file gives you more control over the jobs that the bot applies to. There are two sections: # Job Title Filters and # Job Description Filters , these must be included on the document, these names are hardcoded on the script do not modify them.

• resume.pdf. Will be uploaded to LinkedIn when applying. The resume file can have a different name as long as it is a pdf file and the name contains the word resume. E.g. Michael_Scott_Resume.pdf.

• cover_letter.pdf. Will be uploaded to LinkedIn when applying if provided and the job application asks for it. The cover letter file can have a different name as long as it is a pdf file and the name contains the word cover. E.g. Dwight_Schrute_Cover_Letter.pdf.

  The # Job Title Filters section is used to filter the job title, and the # Job Description Filters section is used to filter the job description (once the job passes the job title filtering). The information on these sections is used on different steps of the process, you can have different rules on each section, or the same rules on both sections.

  Use natural language to explain what you are interested in, and what you are not. The LLM will try to understand what you mean, and will decide to apply or not to the job.

An config/output folder will be created, where you will find all generated answers to the questions.

The folder approach enables you to have multiple configurations (based on locations, roles...), and switch between them easily.

You will find templates for all this files in the Templates folder.

Install required libraries

You should use a virtual environment for this, but it is not required.

pip3 install -r requirements.txt

Execute

To run the bot, run the following in the command line, providing the path to your personal information directory as only argument.

#copy template example data
cp Templates data

#Fill it with your own data

python3 main.py $(pwd)/config

data/Config.yaml Explanations

Just fill in your email and password for linkedin.

email: email@domain.com
password: yourpassword

This prevents your computer from going to sleep so the bot can keep running when you are not using it. Set this to True if you want this disabled.

disableAntiLock: False

Set this to True if you want to look for remote jobs only.

remote: False

This is for what level of jobs you want the search to contain. You must choose at least one.

experienceLevel:
 internship: False
 entry: True
 associate: False
 mid-senior level: False
 director: False
 executive: False

This is for what type of job you are looking for. You must choose at least one.

jobTypes:
 full-time: True
 contract: False
 part-time: False
 temporary: False
 internship: False
 other: False
 volunteer: False

How far back you want to search. You must choose only one.

date:
 all time: True
 month: False
 week: False
 24 hours: False

A list of positions you want to apply for. You must include at least one.

positions:
 #- First position
 #- A second position
 #- A third position
 #- ...

A list of locations you are applying to. You must include at least one.

locations:
 #- First location
 #- A second location
 #- A third location
 #- ...
 - Remote

How far out of the location you want your search to go. You can only input 0, 5, 10, 25, 50, 100 miles.

distance: 25

A list of companies to not apply to.

companyBlacklist:
 #- company
 #- company2

A list of words that will be used to skip over jobs with any of these words in there.

titleBlacklist:
 #- word1
 #- word2

Input your personal info. Include the state/province in the city name to not get the wrong city when choosing from a dropdown. The phone country code needs to be exact for the one that is on linkedin. The website is interchangeable for github/portfolio/website.

This information should also be provided on Templates/personal_data.md.

# ------------ Additional parameters: personal info ---------------
personalInfo:
 First Name: FirstName
 Last Name: LastName
 Phone Country Code: Canada (+1) # See linkedin for your country code, must be exact according to the international platform, i.e. Italy (+39) not Italia (+39)
 Mobile Phone Number: 1234567890
 Street address: 123 Fake Street
 City: Red Deer, Alberta # Include the state/province as well!
 State: YourState
 Zip: YourZip/Postal
 Linkedin: https://www.linkedin.com/in/my-linkedin-profile
 Website: https://www.my-website.com # github/website is interchangeable here

Known issues

• The bot not always replaces correctly the placeholders on the cover letter.
• If any field has problems with the answer, e.g. expected a number and the bot generated a text, the application will not proceed.
• Usually the first screen asking for contact information also ask for a summary, gpt doesn't fill this screen, so the application will not proceed.