# Invoice Extract

> Invoice Extract & Summarize

- Skill: `jiahao1553/invoice-extract` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add jiahao1553/invoice-extract`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jiahao1553/invoice-extract/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: jiahao1553 (https://skillmd.com/u/jiahao1553)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jiahao1553/invoice-extract

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# Invoice Extract & Summarize

Turns a folder of invoice PDFs into a verified Excel workbook (Summary + Line Items sheets).

## Prerequisites

None — no MCP required. Only needs the `openpyxl` Python package (installed in step 4 if missing).

## When to use
User wants invoice PDFs in the project directory read and turned into a cost/price summary table (Excel/xlsx). Rerun this any time new invoices are added — it re-extracts from scratch, it does not incrementally update an old workbook.

## Steps

1. **Find the invoices.** `Glob **/*.pdf` (or a narrower pattern like `**/*[Ii]nvoice*.pdf` if the user names one) under the target directory. Confirm the file list with the user only if it's ambiguous which PDFs are invoices vs unrelated PDFs.

2. **Batch and delegate extraction.** For more than ~4-5 invoices, split into batches of 3-4 and spawn parallel `general-purpose` Agent calls (not forks — these need no prior context). Each agent's prompt must:
   - List its exact batch of file paths (use forward-slash paths, e.g. `/c/repos/<project>/...`, they work fine with Read).
   - Instruct it to Read each PDF and extract: invoice no, invoice date, visit date, department/clinic, vendor, patient/bill-to, every line item (description, qty/unit, amount before subsidy, amount after subsidy — or whatever the analogous discount/tax structure is), subtotal, subsidy/discount, pre-tax total, tax/GST amount, total payable, any Medisave/other deduction, amount paid, final amount payable.
   - Tell it to double check every number against the actual PDF text (invoices have easy-to-transpose digits) and to return a **plain-text or markdown structured block per invoice**, not prose paragraphs, so you can parse it reliably.
   - For small batches (<=5 total invoices), skip delegation and just Read the PDFs directly yourself.

3. **Consolidate into JSON.** Merge all batch results into one JSON object matching the schema documented at the top of `scripts/build_xlsx.py`: `{"invoices": [...], "line_items": [...]}`. Write it to a scratch file (use the session scratchpad directory).

4. **Verify before building.** Run `python scripts/build_xlsx.py <input.json> <output.xlsx>` — it runs an arithmetic reconciliation pass first (line-item sum vs subtotal, subtotal+subsidy vs pre-tax total, 9%-style GST recompute vs stated GST) and prints any mismatches before saving. If it reports mismatches, do not report success to the user — go back to the source PDF for that invoice and correct the figure, don't just suppress the warning.
   - If the invoice's tax scheme isn't a flat percentage (no GST-style rate, or a different rate), skip the GST-recompute check but still verify the line-item-sum-vs-subtotal check, which is scheme-agnostic.
   - `openpyxl` must be installed (`pip install openpyxl` if missing).

5. **Report.** Tell the user the output path, invoice count, sheet names, and call out anything that needed manual correction during verification (don't silently fix without mentioning it).

## Notes
- Works on whatever project directory Claude is invoked in — point it at any folder of invoice PDFs.
- The build script is generic and reusable on its own: any caller that produces the JSON schema it expects can run it directly without re-deriving the xlsx-building code.