LinkedinPython — Job Intelligence Dashboard

Scrape LinkedIn jobs, score them against your resume with the AI provider of your choice (Anthropic, DeepSeek, Gemini, OpenAI, Llama, or any OpenAI-compatible endpoint), and get a personalised skill gap study plan. Works for any profession, any location.

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Features

Mode	What you get
🔍 Scraper Only	Jobs → CSV with title, company, location, salary, URL
🎯 Scraper + AI Score	Above + fit score, response probability, matched/missing skills, verdict
🧠 Full Analysis	Above + skill gap clusters ranked by score boost + 4-week study plan

• Choose your AI provider & model from dropdowns — Anthropic, DeepSeek, Gemini, OpenAI, Llama, or a custom OpenAI-compatible endpoint
• 🔬 Test Run — a ~30s preflight: logs in, scores one real job, then cleans up. Run it before committing to a full scrape
• Speed control — Fast / Balanced / Safe pacing (all keep human-like jitter to avoid rate limits)
• Parallel AI scoring — jobs are scored concurrently (worker count auto-tuned per provider), so scoring is near-instant
• Jobs sorted by fit score inside 3 salary tabs: Annual / Hourly / Not Listed
• Stop mid-run and save partial results; Run starts a clean new session
• Works on macOS, Linux, and Windows (no WSL needed)
• Windows: setup.bat creates a one-click Desktop shortcut that launches the dashboard (and self-installs any missing packages)

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Quick Start

Option A: Docker (recommended — zero dependency install)

git clone https://github.com/jarvez31/LinkedinPython
cd LinkedinPython
# Create a .env file with your credentials:
cp .env.example .env
# Edit .env — add your LinkedIn + LLM credentials
docker compose up --build

Open http://localhost:5000. Works identically on Windows, macOS, and Linux.

Option B: Manual (Python + Playwright)

macOS / Linux

git clone https://github.com/jarvez31/LinkedinPython
cd LinkedinPython
bash setup.sh

Windows

git clone https://github.com/jarvez31/LinkedinPython
cd LinkedinPython
setup.bat

2. Add your credentials

Edit the .env file and fill in your details:

LINKEDIN_EMAIL=your-email@example.com
LINKEDIN_PASSWORD=your-password
# Provider: anthropic | deepseek | gemini | openai | llama | custom
LLM_PROVIDER=anthropic
LLM_MODEL=claude-sonnet-4-6
LLM_API_KEY=sk-ant-api03-...
# Custom endpoints only (deepseek/gemini/llama are routed automatically):
# LLM_BASE_URL=https://api.openrouter.ai/v1

You can also pick the provider, model, and API key directly in the dashboard — no .env edit required. Non-Anthropic providers use the openai package (already in requirements.txt).

3. Run

source venv/bin/activate   # macOS / Linux
venv\Scripts\activate      # Windows
python app.py

Open http://localhost:5000 in your browser.

4. Smoke test (optional)

Before a full run, verify everything works:

python smoke_test.py

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Usage

1. Select a mode in the dashboard
2. Enter your LinkedIn credentials (pre-filled from .env)
3. Set keywords, location, pages per keyword, time filter, and Speed (Fast / Balanced / Safe)
4. Enter your profession or field — the AI uses this to tailor scoring and the study plan
5. Upload your resume (PDF or DOCX) — required for scoring modes
6. Pick your AI provider & model and enter the API key (pre-filled from .env) — required for scoring modes
7. (Recommended) Hit 🔬 Test Run first — logs in, scores one job, confirms everything works in ~30s
8. Click Run Pipeline — watch the live log (including live token counts per scored job)
9. Hit ⚠ Stop to halt and save partial results, ↺ Reset to start fresh
10. Download output files or browse the salary tabs

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Output Files

Saved to outputs/ folder.

File	Contents
job_results_YYYY-MM-DD.csv	Jobs in 3 sections by salary type, sorted by fit score
skill_clusters.txt	Skill gaps ranked by how much learning each one boosts your score
study_plan.txt	4-week day-by-day learning roadmap tailored to your gaps

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Requirements

• Python 3.9+
• A LinkedIn account
• An API key for one AI provider (scoring modes only): Anthropic, DeepSeek, Gemini, OpenAI, Llama, or any OpenAI-compatible endpoint. DeepSeek is the cheapest; Anthropic ~$5 covers 250–300 scored jobs.

Secrets are stored in .env (gitignored). Preferences (keywords, location, profession) are saved to config.json for convenience.

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Cost

Mode	Cost
Scraper Only	Free
Scraper + AI Score (100 jobs)	~$1.00-2.00
Full Analysis (100 jobs)	~$1.50-2.50

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Project Structure

├── app.py                    ← Flask server + full pipeline (scrape, score, clusters, plan)
├── dashboard.html            ← Browser UI
├── launch_dashboard.bat      ← Windows one-click launcher (Desktop shortcut target; self-heals deps)
├── smoke_test.py             ← Pre-flight dependency check
├── clean_jobs.py             ← Remove null-description jobs from data files
├── setup.sh / setup.bat      ← One-command setup (Windows setup.bat also creates the Desktop shortcut)
├── requirements.txt
├── .env.example              ← Template for your credentials
│
├── data/                     ← Job databases (gitignored)
├── outputs/                  ← Generated files (gitignored)
└── attachments/              ← Resume uploads (gitignored)

Want the full picture? See ARCHITECTURE.md for how a run flows end-to-end, the modes, the supported AI providers, and the HTTP endpoints.

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Notes

• Uses Playwright (headless Chromium) with rate limiting — works on residential IPs, not cloud servers
• Speed tiers keep human-like randomness; if the browser session dies, the scrape auto-aborts and saves partial results (no more multi-hour hangs)
• Scoring runs in parallel with a per-provider auto-tuned worker count; the live log shows token counts per job
• Jobs deduplicated by ID on every run — re-runs only add new jobs
• Salary auto-classified: annual / hourly / missing
• Run python smoke_test.py (or the in-app Test Run) before your first full pipeline to catch setup issues early