document-parser

name	document-parser
description	OCR and parse documents including invoices, receipts, and bank statements. Use when extracting text from PDF/images, parsing scanned documents, or processing uploaded files. Supports Google Document AI and Tesseract OCR. Use when this capability is needed.
metadata	{"author":"alexi5000"}

Document Parser Skill

Purpose

Extracts text and structured data from documents using OCR technology, enabling automated processing of invoices, receipts, statements, and contracts.

Triggers

• PDF or image document uploaded
• Scanned invoice needs processing
• Receipt needs expense categorization
• Bank statement needs reconciliation

Capabilities

1. OCR Text Extraction - Convert images to text
2. PDF Parsing - Extract text from PDF documents
3. Structured Data Extraction - Identify fields, tables, line items
4. Document Classification - Classify document type
5. Quality Assessment - Assess OCR confidence

Instructions

Step 1: Document Type Detection

Classify document:

• Invoice - Vendor bill
• Receipt - Proof of purchase
• Bank Statement - Monthly statement
• Contract - Legal agreement
• Other - Unknown type

Use file name, content patterns, or LLM classification.

Step 2: OCR Processing

Google Document AI (Preferred)

const document_ai = require('@google-cloud/documentai');
const client = new document_ai.DocumentProcessorServiceClient();

const [result] = await client.processDocument({
  name: processor_name,
  rawDocument: {
    content: file_buffer.toString('base64'),
    mimeType: 'application/pdf',
  },
});

const extracted_text = result.document.text;
const entities = result.document.entities;  // Pre-extracted fields

Tesseract OCR (Fallback)

tesseract invoice.png output -l eng

Step 3: Field Extraction

For Invoices, extract:

• Vendor name and address
• Invoice number and date
• Due date
• Line items (description, quantity, price)
• Subtotal, tax, total

For Receipts, extract:

• Merchant name
• Date and time
• Items purchased
• Total amount

For Bank Statements, extract:

• Account number
• Statement period
• Transaction list (date, description, amount)
• Beginning and ending balance

Step 4: Structured Output

Return JSON with confidence scores:

{
  "document_type": "invoice",
  "confidence": 0.92,
  "raw_text": "...",
  "extracted_fields": {
    "vendor_name": "Office Depot",
    "invoice_number": "INV-2024-001",
    "invoice_date": "2026-01-15",
    "due_date": "2026-02-15",
    "total_amount": "250.00",
    "currency": "USD",
    "line_items": [
      {
        "description": "Printer Paper",
        "quantity": "10",
        "unit_price": "15.00",
        "total" 
      
    
  
   
     
     
     
  
   

Step 5: Quality Check

Assess quality:

• High Confidence (> 0.9) - Auto-process
• Medium Confidence (0.7 - 0.9) - Flag for review
• Low Confidence (< 0.7) - Require manual entry

Check for:

• Missing required fields
• Illegible text
• Poor image quality
• Incomplete document

Step 6: Post-Processing

• Normalize Data - Standardize dates, amounts
• Validate - Check logical consistency
• Enhance - Add context from database (e.g., known vendor)

Error Handling

• OCR Failed - Return error, suggest higher quality scan
• Unsupported Format - Return error, list supported formats
• Encrypted PDF - Request password or unlocked version
• Large File - Split into pages, process individually
• Poor Quality - Suggest rescan, adjust DPI

File Type Support

Type	Extension	OCR Required
PDF (text)	.pdf	No
PDF (scanned)	.pdf	Yes
Image	.png, .jpg, .jpeg	Yes
Not Supported	.doc, .docx, .xls	Convert first

Integration Points

• Google Document AI - Primary OCR engine
• Tesseract - Fallback OCR
• invoice-parser (AP worker) - For invoice-specific parsing
• data-validator (Data worker) - For validation

Models

• OCR: Google Document AI or Tesseract
• Classification: Claude Sonnet 4 or Gemini Flash
• Field Extraction: Claude Sonnet 4 (when OCR confidence low)

Security

• Validate file type and size before processing
• Scan for malware (if uploaded by user)
• Never store raw documents longer than needed
• Redact PII from logs
• Encrypt documents at rest

Performance

• Target: < 10s for invoice OCR
• Batch Processing: Process up to 50 invoices concurrently
• Caching: Cache OCR results for 24h (in case reprocessing needed)

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Invoke this skill as the first step when processing any scanned or PDF document.

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