Python sandbox for healthcare automation patterns using Playwright scraping, OpenClaw-style parallel extraction, and optional LLM/CrewAI reporting workflows.

• Browser automation examples with Playwright (healthcare_automation.py, advanced_playwright.py)
• OpenClaw-style concurrency demos (openclaw_final_working.py, openclaw_final.py, openclaw_healthcare.py)
• LLM reporting flows (OpenAI and Ollama variants)
• Crew-based multi-agent healthcare analysis scripts
• Generated artifacts (Excel, JSON, HTML, TXT reports)

Windows PowerShell:

python -m venv .venv
.venv\Scripts\Activate.ps1

pip install pandas openpyxl playwright tenacity requests
python -m playwright install

python openclaw_final_working.py

Expected outputs include:

• openclaw_healthcare_report.xlsx
• openclaw_results.json
• PORTFOLIO.txt
• INTERVIEW_OPENCLAW_SCRIPT.txt

python healthcare_automation.py

Generates:

• staff_report.xlsx

python llm_reporting.py

Generates:

• ai_generated_report.html
• ai_summary.txt

Scripts such as healthcare_crew_ollama.py and ollama_reporting.py target a local Ollama server (http://localhost:11434) and compatible local models.

This repo includes multiple experimental tracks. Install what you need:

• Base: pandas, openpyxl, playwright, tenacity, requests
• OpenAI track: openai
• CrewAI track: crewai, crewai_tools, langchain-community
• OpenClaw/CMDOP experiments: openclaw, cmdop (if you intend to run related scripts)

• openclaw_final_working.py: main production-style OpenClaw demo pipeline
• openclaw_final.py: similar pipeline with richer console output
• healthcare_automation.py: Playwright extraction against OrangeHRM demo
• llm_reporting.py: OpenAI summarization/anomaly reporting on extracted data
• healthcare_crew.py / healthcare_crew_ollama.py: crew-based analysis flows
• test_playwrite.py, test_ollama.py: basic connectivity/test scripts

• Do not hardcode API keys in source files.
• Use environment variables for credentials (OPENAI_API_KEY, tool-specific API keys).
• Treat files like auth.json and generated reports as sensitive.
• Review .gitignore before committing local credentials or artifacts.

1. Run extraction (healthcare_automation.py or openclaw_final_working.py)
2. Validate output Excel/JSON files
3. Run LLM analysis (llm_reporting.py or Ollama/Crew scripts)
4. Share generated report artifacts