Skill Matcher and Cover Letter Generator

A config-driven Streamlit app that matches your resume with job postings and generates tailored cover letters. Works with any OpenAI-compatible LLM API.

Features

• Paste a job URL or job description text to extract job details
• Match your resume against job requirements with skill gap analysis
• Generate and download personalized cover letters as PDF
• Upload PDF/MD documents via sidebar — auto-discovered by the app
• All prompts and profile data are config-driven (no hardcoded data)
• Supports any OpenAI-compatible API (OpenRouter, Claude proxy, local LLMs, etc.)

Project Structure

├── app.py                 # Streamlit UI (sidebar upload, job input, cover letter)
├── chains.py              # LLM chains (prompts loaded from config.yml)
├── config_loader.py       # Config loading + validation
├── profile_data.py        # Resume data loading with auto-discovery
├── utils.py               # Text extraction (PDF/MD), PDF generation
├── config.yml.example     # Config template (profile, prompts, resources)
├── .env.example           # API env vars template
├── dockerfile             # Docker image build
├── docker-compose.yml     # Local dev compose
├── deploy.sh              # Build & push to Docker Hub (ARM64)
├── deploy/                # Cloud deployment templates (git-ignored)
│   ├── docker-compose.yml
│   ├── .env.example
│   └── config.yml.example
└── data/                  # Resume/document files (git-ignored)

Quick Start

1. Clone & configure

git clone https://github.com/Ring8688/SkillMatcher-CoverLetterGenerator.git
cd SkillMatcher-CoverLetterGenerator

cp config.yml.example config.yml   # Fill in your personal info & prompts
cp .env.example .env               # Fill in your API key & endpoint

2. Add your resume files

Place your .pdf or .md files in the data/ directory:

mkdir -p data
cp /path/to/your/cv.md data/

Update config.yml resources section to point to your files, or simply drop them in data/ — they will be auto-discovered.

3. Run locally

pip install -r requirements.txt
streamlit run app.py

Or with Docker:

docker compose up --build

App runs at http://localhost:8501.

Configuration

.env — API settings

API_BASE_URL=https://openrouter.ai/api/v1
API_KEY=your-api-key-here
API_MODEL=gpt-4o
API_TEMPERATURE=0

config.yml — Profile & prompts

Key sections:

Section	Description
personal	Name, email, phone, location
links	LinkedIn, portfolio, GitHub
education	University, degree, GPA
experience	Current role, tech stack, summary
resources	Explicit list of resume files to load
cover_letter	Value propositions, tone, max words
prompts	4 LLM prompt templates (extract_jobs, write_match, extract_personal_info, cover_letter)
app	UI title, caption, default job URL

Cloud Deployment (Docker)

Build & push image

./deploy.sh   # Builds linux/arm64 → ring8688/jasper-server:coverlettertool

Deploy on cloud server

mkdir -p ~/coverlettertool/data
cd ~/coverlettertool

# Copy templates from deploy/ directory and fill in your values
cp .env.example .env
cp config.yml.example config.yml

# Add resume files to data/

docker compose pull
docker compose up -d

The deploy/ directory contains ready-to-use templates with cloud networking (external cloud_net network).

Single Docker mount: ./data:/app/data — uploaded files persist across container restarts.

Tech Stack

• Frontend: Streamlit
• LLM: LangChain + any OpenAI-compatible API
• PDF: PyPDF (reading), FPDF (generation)
• Config: YAML + python-dotenv
• Deploy: Docker (ARM64), Docker Compose