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Your companion for customizing resumes and preparing for interviews.
ResumeCrew is an AI-powered tool that helps job seekers tailor their resumes and prepare for interviews using CrewAI.
ResumeCrew streamlines your job application process through four key steps:
- 🔍 Job Posting Analysis: Extracts key requirements from the job posting.
- 👤 Candidate Profiling: Creates a comprehensive profile using your GitHub and personal information.
- 📝 Resume Tailoring: Customizes your resume to highlight relevant skills and experiences.
- 🎤 Interview Preparation: Generates potential questions and talking points for your interview.
Each step is powered by AI to provide you with tailored, insightful results for your job application.
graph TD
A((Start)) --> B[Research Task]
A --> C[Profile Task]
B --> D[Resume Strategy Task]
C --> D
B --> E[Interview Preparation Task]
C --> E
D --> E
D --> F[[output/tailored_resume.md]]
E --> G[[output/interview_materials.md]]
F --> H((End))
G --> H
- Clone the repository:
git clone https://github.com/alexnodeland/resume-crew.git
- Install the dependencies:
poetry install
-
Copy the
.env.example
file to.env
and fill in the required environment variables. -
(Optional) Modify the
data/cli-default.json
file to customize defaults to your specific data, including:applicant_name
: Your full name.job_posting_url
: The URL of the job posting you are applying to.github_url
: The URL of your GitHub profile.personal_writeup
: A brief writeup about your professional background and skills.
-
Place your resume in the
data
directory, and name itresume.md
. -
Choose one of the following options to run the application:
- Run the CLI application:
poetry run resume-crew-cli
-
Follow the CLI prompts to use the application, or press
Enter
to use the default values set incli-default.json
. -
Visit the
output
directory to view the generated resume and interview materials.
- Run the Streamlit app:
poetry run resume-crew
Contributions are welcome! Please read the contributing guidelines first.
This project was adapted from an example in the course Multi AI Agent Systems with crewAI. I would like to extend our gratitude to the course creators João Moura, CrewAI, and Deeplearning.AI for providing such a comprehensive and insightful resource.