🚀 Job SERP Explorer

Find jobs. Parse meaningfully. Apply strategically.

Job SERP Explorer is an open-source tool for efficient, semi-automated job search. Instead of manually skimming dozens of job boards, this tool helps you search, extract, rank, and organize job listings — tailored to your preferences. You can even edit how job postings are triaged for you and evaluated, with an intuitive interface and editable prompt logic.

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🔍 What It Solves

Manual job search is noisy, fragmented, and time-consuming. This tool lets you:

• Run intelligent searches (e.g. "Data Scientist" in "Remote" or "Germany")
• Parse job ads and extract structured data (title, company, visa info, etc.)
• Apply AI scoring with custom rules & prompt templates
• View tables of top-matching jobs and actionable application URLs
• Re-run, edit, and refine past searches anytime

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📦 Live Demo

🔗 Streamlit Cloud Demo
or
💻 Run locally: see setup below

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🛠 Quickstart: Run Locally

1. Clone and install

git clone https://github.com/yourname/jobserp-explorer.git
cd jobserp-explorer
pip install -e .

(Use a Python 3.9+ virtual environment)

2. Launch the app

streamlit run jobserp_explorer/app.py

Then open: http://localhost:8501

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🧠 Custom Prompts & Parsers

This tool gives you full control over what is extracted and how it's scored.

Editable schema parser

• Navigate to 🔧 Schema Editor
• Modify or extend what fields get extracted (e.g., visa info, remote policy, stack)

Prompt customization

• Navigate to ✍️ Prompt Editor
• Tailor the job evaluation criteria (e.g., mission alignment, tech fit, location)

You’re not locked into fixed logic — you define what “a good job” means for you.

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🗂 Folder Structure

jobserp_explorer/
├── core/                      # CLI pipelines
├── data/                      # Run outputs
├── prompts/                  # Prompt templates
├── schemas/                  # Parsing schemas
├── app.py                    # Main Streamlit UI
└── ...

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🤝 Contributing

We welcome contributions from job seekers, developers, and prompt engineers.

• 🧪 Check the Issues tab for good-first-issue
• 🧠 Suggest new schema fields or scoring prompts
• 🧼 Improve UI layout or add new data sources

Local development

pip install -e ".[dev]"
pre-commit install

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Made with ❤️ by matuteiglesias 🧭 “Automate the boring. Apply where it counts.”