AI-powered resume tailoring using RenderCV and local LLMs. Generate customized resumes for specific job descriptions while maintaining full control over your data.

• Multiple LLM Providers: Supports both local (Ollama) and cloud (Google Gemini) LLMs
• RenderCV Powered: Professional PDF generation with multiple themes
• Bold Emphasis: Automatically emphasizes key achievements and technologies
• Modular Architecture: Swap LLM providers, customize prompts, extend easily
• Batch Processing: Generate resumes for multiple jobs at once
• Fully Customizable: Control every aspect of resume generation

• Python 3.10 or higher
• LLM Provider (choose one or both):
  • Ollama for local/privacy-focused LLM
  • Google Gemini API key for cloud-based LLM
• A LaTeX distribution (for RenderCV PDF generation)

# Clone the repository
git clone https://github.com/yourusername/resume-tailor.git
cd resume-tailor

# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create virtual environment with uv
uv venv

# Activate virtual environment
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode with uv
uv pip install -e ".[dev]"

# Install Ollama and pull model
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.1:8b

# Clone the repository
git clone https://github.com/yourusername/resume-tailor.git
cd resume-tailor

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e ".[dev]"

# Install Ollama and pull model
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.1:8b

# Create a starter resume template
resume-tailor init "Your Name"

# This creates Your_Name_CV.yaml - edit it with your information

Or copy the template files from examples/ to source/ directory:

# Copy templates to source directory (your personal resume data)
cp -r examples/* source/
# Then edit source/base_resume.yaml and source/static_sections.yaml with your information

Note: The source/ directory is gitignored to protect your personal information.

# Single job description
resume-tailor generate source/job_descriptions/senior_backend.txt

# Custom output location
resume-tailor generate job.txt -o ./resumes/google

# Use different model
resume-tailor generate job.txt --model mistral:7b

# Use different theme
resume-tailor generate job.txt --theme classic

# Skip PDF rendering (YAML only)
resume-tailor generate job.txt --no-render

# Process multiple job descriptions at once
resume-tailor batch ./job_descriptions/ -o ./output

# Render your base resume without any LLM tailoring
# Useful for testing design changes quickly
resume-tailor original

# Custom output location
resume-tailor original -o ./test_design

# Renders: output/original/original_resume.yaml and PDF

# View current settings
resume-tailor info

1. Load Base Resume: Your master resume with all experience and skills
2. Extract Keywords: LLM analyzes job description for key terms
3. Tailor Sections: LLM rewrites experience/skills to emphasize relevant points
4. Add Bold Emphasis: Automatically bolds key achievements and technologies
5. Merge & Render: Combines tailored sections with static info, generates PDF

resume-tailor/
├── src/resume_tailor/
│   ├── cli/                # Command-line interface
│   │   └── commands.py     # Typer CLI commands
│   ├── core/               # Business logic
│   │   ├── service.py      # Main orchestrator
│   │   └── template.py     # YAML merging
│   ├── llm/                # LLM abstraction
│   │   ├── base.py         # Abstract interface
│   │   ├── ollama.py       # Ollama implementation
│   │   ├── gemini.py       # Google Gemini implementation
│   │   ├── mock.py         # Mock provider for testing
│   │   └── prompts.py      # Prompt templates
│   ├── renderer/           # PDF generation
│   │   └── rendercv.py     # RenderCV wrapper
│   ├── config/             # Configuration
│   │   ├── schemas.py      # Pydantic models
│   │   └── settings.py     # Settings management
│   ├── utils/              # Utilities
│   │   └── logger.py       # Logging setup
│   ├── __init__.py         # Package init
│   └── __main__.py         # Entry point for python -m
├── source/                 # Your personal resume files (gitignored)
│   ├── base_resume.yaml    # Your full master resume
│   ├── static_sections.yaml # Your static info (name, education)
│   └── job_descriptions/   # Your job postings
├── examples/               # Example/template files (committed to repo)
│   ├── base_resume.yaml    # Template resume with placeholder data
│   ├── static_sections.yaml # Template static sections
│   └── job_descriptions/   # Sample job postings
├── tests/                  # Unit tests
│   ├── test_core/          # Core logic tests
│   ├── test_llm/           # LLM provider tests
│   ├── test_integration/   # Integration tests
│   └── conftest.py         # Pytest fixtures
├── .env.example            # Example environment configuration
├── .env                    # Your environment config (gitignored)
├── README.md               # This file
└── pyproject.toml          # Project metadata and dependencies

Create a .env file to customize settings:

RESUME_TAILOR_LLM_PROVIDER=ollama
RESUME_TAILOR_LLM_MODEL=gemma3:27b
RESUME_TAILOR_LLM_BASE_URL=http://localhost:11434
RESUME_TAILOR_LLM_TEMPERATURE=0.3
RESUME_TAILOR_BASE_RESUME_PATH=source/base_resume.yaml
RESUME_TAILOR_STATIC_SECTIONS_PATH=source/static_sections.yaml
RESUME_TAILOR_RENDERCV_THEME=engineeringresumes
RESUME_TAILOR_OUTPUT_DIR=./output

RESUME_TAILOR_LLM_PROVIDER=gemini
RESUME_TAILOR_LLM_MODEL=gemini-2.5-flash
RESUME_TAILOR_GEMINI_API_KEY=your_api_key_here
RESUME_TAILOR_LLM_TEMPERATURE=0.3
RESUME_TAILOR_BASE_RESUME_PATH=source/base_resume.yaml
RESUME_TAILOR_STATIC_SECTIONS_PATH=source/static_sections.yaml
RESUME_TAILOR_RENDERCV_THEME=engineeringresumes
RESUME_TAILOR_OUTPUT_DIR=./output

Get your Gemini API key at: https://aistudio.google.com/app/apikey

• engineeringresumes (default)
• classic
• sb2nov
• moderncv

Contains information that never changes:

cv:
  name: Your Name
  email: you@example.com
  phone: "+1 (555) 123-4567"
  sections:
    education:
      - institution: University Name
        degree: BS
        area: Computer Science

Your complete master resume with all experience:

cv:
  sections:
    experience:
      - company: Company Name
        position: Senior Engineer
        start_date: "2020-01"
        highlights:
          - Led team of 5 engineers
          - Improved performance by 40%
    skills:
      - label: Languages
        details: Python, Go, JavaScript

The tool extracts experience and skills from your base resume, tailors them with the LLM, then merges with static sections.

# Make sure your virtual environment is activated first
# If using uv: source .venv/bin/activate
# If using pip: source venv/bin/activate

# Run all tests
pytest

# With coverage
pytest --cov=src/resume_tailor

# Run specific test file
pytest tests/test_core/test_template.py -v

# Or use the venv directly without activation
.venv/bin/pytest  # For uv-created venv
# OR
venv/bin/pytest   # For pip-created venv

# Format code
black src/ tests/

# Lint
ruff src/ tests/

# Type check
mypy src/

Create a new provider in src/resume_tailor/llm/:

from .base import BaseLLMProvider, LLMMessage, LLMResponse
from typing import List, Iterator, Any

class MyProvider(BaseLLMProvider):
    def __init__(self, model: str, temperature: float = 0.3, **kwargs: Any):
        super().__init__(model, temperature, **kwargs)
        # Initialize your client here

    def chat(self, messages: List[LLMMessage], **kwargs: Any) -> LLMResponse:
        # Your implementation
        pass

    def stream_chat(self, messages: List[LLMMessage], **kwargs: Any) -> Iterator[str]:
        # Your implementation
        pass

Then add it to cli/commands.py in the create_service function. See gemini.py and ollama.py for examples.

Edit src/resume_tailor/llm/prompts.py to adjust how the LLM tailors content. The prompts control:

• How experience highlights are rewritten
• Which keywords are emphasized
• How skills are prioritized
• Bold formatting rules

You can customize the PDF appearance by adding a design section to your base_resume.yaml:

cv:
  sections:
    # Your resume content here...

design:
  theme: engineeringresumes
  page:
    size: us-letter
    top_margin: 1.5cm
    bottom_margin: 1.5cm
    left_margin: 1.5cm
    right_margin: 1.5cm
    show_page_numbering: false
    show_last_updated_date: false  # Remove "last updated" date
  text:
    font_family: Source Sans 3
    font_size: 12pt  # Minimum 12pt for readability
    leading: 0.55em
    alignment: justified
  colors:
    text: rgb(0, 0, 0)
    name: rgb(0, 79, 144)
    section_titles: rgb(0, 79, 144)
    links: rgb(0, 79, 144)
  # ... more customization options

Key Design Options:

• font_size: Recommended minimum 12pt for ATS readability
• show_last_updated_date: Set to false to remove timestamp
• show_page_numbering: Toggle page numbers
• colors: Customize colors (use professional tech-friendly blues/grays)
• margins: Adjust spacing (1.5cm recommended)

Testing Design Changes:

# Quickly render your base resume with design changes (no LLM processing)
resume-tailor original -o ./test_design

See the full design schema in RenderCV docs: https://docs.rendercv.com

pip install rendercv

Ensure Ollama is running:

ollama serve

Install a LaTeX distribution:

• macOS: brew install --cask mactex
• Ubuntu/Debian: sudo apt-get install texlive-full
• Windows: Install MiKTeX

MIT License - see LICENSE file for details

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

• Multiple LLM providers (Ollama, Gemini)
• Add OpenAI/Anthropic provider support
• Implement ATS scoring
• Create interactive TUI
• Add cover letter generation
• Support multiple resume profiles
• Web interface for non-technical users

• RenderCV for PDF generation
• Ollama for local LLM inference
• Typer for the CLI framework