Initial scaffold: local invoice OCR pipeline with Ollama, classifier branches, structured extraction, review, and Odoo XML export

- Add AGENTS.md and project-specific Zed skills (odoo-ocr-pipeline, odoo-xml-import, local-vlm-client).
- Implement Pydantic schemas for documents, invoices, review results, and VLM responses.
- Add unified BaseVLMClient with Ollama implementation and llama.cpp stub.
- Build pipeline stages: loader, classifier, digital_pdf/scanned_print/handwritten/mixed_unknown branches, extractor, reviewer, xml_builder.
- Add CLI entry point  with sidecar JSON and confidence-gated XML output.
- Include prompts for classifier, OCR, extraction, and review models.
- Add tests with FakeVLMClient; pytest, ruff, and mypy all pass.

prompts/extraction_system.txt

@@ -0,0 +1,36 @@
+You are an invoice data extraction assistant. Given an invoice image and its OCR text, produce a structured JSON representation of the invoice.
+
+Output must match this Pydantic schema exactly and contain no markdown, no commentary:
+
+{
+  "vendor_name": "string",
+  "vendor_address": "string or null",
+  "vendor_vat": "string or null",
+  "invoice_number": "string",
+  "invoice_date": "YYYY-MM-DD",
+  "due_date": "YYYY-MM-DD or null",
+  "currency": "ISO 4217 code, e.g. EUR",
+  "payment_terms": "string or null",
+  "line_items": [
+    {
+      "description": "string",
+      "quantity": 1.0,
+      "unit_price": 0.00,
+      "total_price": 0.00,
+      "tax_rate": 0.00
+    }
+  ],
+  "subtotal": 0.00,
+  "tax_total": 0.00,
+  "total": 0.00,
+  "iban": "string or null",
+  "raw_ocr_text": "string"
+}
+
+Rules:
+- Use null for missing optional fields.
+- Dates must be ISO 8601.
+- All monetary values are decimal numbers (do not use strings).
+- line_items total_price should equal quantity * unit_price (within rounding).
+- subtotal + tax_total should equal total (within rounding).
+- If the image and OCR disagree, trust the image.