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document-processing

Use when the deliverable is a document's bytes or its literal content — text/tables out of PDFs, AcroForm fill and flatten, page merge/split, PDF/DOCX from templates, OCR of image-only scans. NOT schema-typed fields pulled from text (that is structured-extraction), NOT signature routing (e-signature) or spreadsheet cells/formulas (spreadsheet-ops).

skills/document-processing/SKILL.md

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# Document processing

File in, content out — or data in, file out. You open a byte stream (PDF, DOCX, scan) and either pull the content out, or you build a new document from a template and a data dict. That is the whole job: the deliverable is **bytes of a document or the literal content of one**.

The boundary test, apply it first:

- Deliverable is **raw text / Markdown / table cells / a generated file** → you are in the right place.
- Deliverable is **a typed object matching a schema** (`{parties: [...], total: 1234.50}`) → that is `structured-extraction`. This skill stops at "clean Markdown out of the file"; the schema-constrained extraction runs on that Markdown.

Everything else routes too: signing with an audit trail → `e-signature`, spreadsheet grids/formulas/XLSX-as-data → `spreadsheet-ops`, indexing for cross-document Q&A → `rag` (this skill *produces* the text `rag` ingests, it does not index it), downloading the files off a site → `data-scraper`.

## Step 0 — does the PDF have a text layer?

The most expensive mistake in this skill is OCR'ing a PDF that already has a text layer. A digital PDF (exported from Word, a browser, a report tool) carries selectable text — extracting it is free, instant, and lossless. OCR is slow, costs money or GPU, and *introduces* errors. **Never OCR a PDF you can extract.**