Python

We just released Synthetic Patient Record Generator — create realistic medical records for testing and development. What it does Generates HIPAA-compliant synthetic patient data with realistic demographics, medical histories, and clinical records. Perfect for healthcare software testing, database seeding, and application development without using real patient information. Features Generate thousands of unique patient records with realistic names, DOB, SSN, and addresses Create comprehensive medical histories including conditions, medications, allergies, and procedures Produce lab results, vital signs, and clinical notes with proper medical terminology Export to CSV, JSON, or Excel formats with configurable field mappings Customize demographic distributions and medical condition prevalence by age/gender Usage python generate_records....

Creates fake social media metrics including likes, shares, comments, and followers with realistic patterns. Perfect for developers building social media dashboards, marketers creating mock reports, and testing analytics tools without real account access. Features Generate fake follower counts with realistic growth patterns Create authentic-looking engagement metrics (likes, comments, shares) Export data in CSV, JSON, or Excel formats Customizable platform selection (Instagram, Twitter, Facebook, TikTok) Configurable time ranges and metric distributions Usage python fake_metrics....

Generates synthetic bank transaction records with realistic amounts, dates, merchant categories, and account details. Perfect for developers building financial applications who need test data without using real customer information. Outputs to CSV, JSON, or Excel formats. Features Generate transactions with realistic amounts following banking patterns Create multiple account types with different spending behaviors Include merchant categories like groceries, utilities, entertainment Control date ranges and transaction frequency parameters Export to CSV, JSON, or Excel with customizable column formats Usage python generate_transactions....

Generates HIPAA-compliant synthetic patient data including demographics, medical history, and clinical records. Perfect for healthcare software testing, database seeding, and application development without using real patient information. Features Generate realistic patient demographics with names, addresses, DOB, SSN Create medical histories with conditions, medications, allergies, and procedures Produce lab results, vital signs, and clinical notes with proper formatting Export to CSV, JSON, or Excel formats with configurable record counts Customize data patterns to match specific healthcare scenarios and demographics Usage python generate_records....

Generates HIPAA-compliant synthetic patient records with realistic demographics, medical histories, and clinical data. Perfect for healthcare software testing, database seeding, and application development without using real patient data. Features Generate thousands of unique patient records with realistic names, DOB, SSN, and addresses Create comprehensive medical histories including conditions, medications, allergies, and procedures Produce lab results, vital signs, and clinical notes with proper medical terminology Export to CSV, JSON, or Excel formats with configurable field mappings Customize demographic distributions and medical condition prevalence by age/gender Usage python generate_records....

Generates HIPAA-compliant synthetic patient data with realistic demographics, medical histories, and clinical records. Perfect for healthcare software testing, database seeding, and application development without using real patient information. Features Generate thousands of unique patient records with realistic names, DOB, SSN, and addresses Create comprehensive medical histories including conditions, medications, allergies, and procedures Produce lab results, vital signs, and clinical notes with proper medical terminology Export to CSV, JSON, or Excel formats with configurable field mappings Customize demographic distributions and medical condition prevalence by age/gender Usage python generate_records....

We just released Professional Network Automation Rule Builder — build automation rules for linkedin engagement without browser scripting. What it does Creates rule-based automation workflows for LinkedIn activities using exported data files. Processes connection requests, messages, and content engagement based on custom criteria. Works with exported LinkedIn data to plan automated actions. Features Parse exported LinkedIn CSV data for contacts and activity history Create conditional rules for connection acceptance based on profile criteria Generate message templates for different contact categories Schedule engagement actions based on time intervals and user behavior Export action plans to standard formats for third-party automation tools Usage python linkedin_rules....

Creates rule-based automation workflows for LinkedIn activities using exported data files. Processes connection requests, messages, and content engagement based on custom criteria. Works with exported LinkedIn data to plan automated actions. Features Parse exported LinkedIn CSV data for contacts and activity history Create conditional rules for connection acceptance based on profile criteria Generate message templates for different contact categories Schedule engagement actions based on time intervals and user behavior Export action plans to standard formats for third-party automation tools Usage python linkedin_rules....

This tool reads your exported ecommerce data (orders, inventory, customers) and identifies process inefficiencies like fulfillment delays, stockouts, and customer churn patterns. It’s for Python developers managing small to mid-sized online stores who want data-driven insights before buying more tools. The key benefit is a clear report showing where to fix your operations first. Features Load and merge multiple CSV/JSON exports from platforms like Shopify or WooCommerce Calculate key metrics: order processing time, inventory turnover, repeat purchase rate Flag anomalies: late shipments, frequent out-of-stock items, high-return customers Generate summary PDF report with charts and prioritized action items Export analysis results to JSON for integration with other systems Usage ecommerce-audit --orders orders....

Automates conversion of academic journal articles from Word format to multiple output formats required by Open Journal Systems. Processes batch files and generates compliant JATS XML for scholarly publishing workflows. Works with locally stored Word documents. Features Converts .docx files to PDF with embedded fonts and proper formatting Generates HTML output with semantic markup for web publication Creates JATS XML files compliant with NLM standards for OJS import Preserves mathematical equations, citations, and bibliographic references Batch processes multiple articles with single command execution Usage python journal_converter....

Processes exported Ebay product data from CSV files to generate organized inventory reports and pricing analysis. Works with Ebay’s export formats to clean and structure your marketplace data for better business insights. Features Parse Ebay CSV export format with multiple column mappings Calculate profit margins and fee breakdowns per item Group products by category, condition, or price range Generate summary reports with sales velocity metrics Export cleaned data to standardized CSV format Usage python ebay_processor....

We just released Ecommerce Process Audit CLI — analyze order, inventory, and customer csv exports to find bottlenecks and waste. What it does This tool reads your exported ecommerce data (orders, inventory, customers) and identifies process inefficiencies like fulfillment delays, stockouts, and customer churn patterns. It’s for Python developers managing small to mid-sized online stores who want data-driven insights before buying more tools. The key benefit is a clear report showing where to fix your operations first....