Create invoices, parse PDFs and images, and generate contracts with PDF export.
npx skills add https://github.com/NalinDalal/operation-nerdev --skill invoice-site
Please help me install this Agent Skill. Skill: invoice-site Source: https://github.com/NalinDalal/operation-nerdev/tree/main Command: npx skills add https://github.com/NalinDalal/operation-nerdev --skill invoice-site
This Skill simplifies the process of creating, parsing, and managing invoices and contracts, saving time and reducing errors.
To create an invoice, run the following command: invoice create --from "John Doe" --to "Jane Smith" --amount 100 --currency USD --date "2023-10-01" --due-date "2023-10-15".
High-intent search queries and answers about installing and using this skill.
To extract data from PDF invoices using OCR, this Skill parses text and structured data from PDFs and images using Tesseract.js and Google Cloud Vision, applying regex for automated processing. It handles invoice parsing directly from uploaded files.
Yes, you can generate a freelance development contract with multiple currencies. The Skill includes a contract generator that supports various currencies and allows exporting the final document as a PDF file for client delivery.
Tesseract.js and Google Cloud Vision are both supported dependencies for invoice parsing. They work together to extract text from PDFs and images, enabling structured data extraction for automated invoice processing workflows.
The best way to create an invoice and export it as a PDF is using the command line interface. You can run a command like `invoice create` with parameters for sender, recipient, amount, currency, and dates to generate the document.
To parse an image file and extract invoice data, the Skill uses OCR libraries to read text from images. It processes the extracted text with regex to structure the data for automated storage and downstream processing.
Regex for invoice data extraction works reliably on consistently formatted invoices but may fail with highly variable layouts. The Skill mitigates this by combining OCR with regex parsing to capture structured data from standard PDF invoices.
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ShunmeiChoExtract text, tables, and form data from PDFs using Python libraries.
