pdf-text-extractlisted

# PDF Text Extract Use this skill when a workflow needs machine-readable text from a digital PDF before summarization, classification, or structured extraction. The helper uses pure-Python PDF libraries when available and includes a simple line-based table detector for downstream cleanup. ## When to invoke - User says: "extract text from this PDF", "feed a PDF to AI", "pull tables from this report", "read this statement". - The PDF already contains selectable text and does not require OCR. ## When NOT to invoke - The PDF is a scanned image; use an OCR workflow instead. - The user needs pixel-perfect table reconstruction with merged cells and layout fidelity. ## Concrete example User input: ```text Extract the text and rough tables from this PDF so Gemini can summarize it. ``` Output: ```python # Copy assets/extract.py into your project, then: from extract import extract_pdf doc = extract_pdf("downloads/report.pdf") print(doc["text"][:2000]) for table in doc["tables"]: print(table["page"], table["rows"][:3]) ``` Install either `pypdf` or `pdfminer.six` in the target project. No OCR binary is required, and the helper fails clearly when the PDF has no extractable text. ## Pattern to apply 1. Prefer digital text extraction first; do not add OCR unless the source is scanned. 2. Preserve page boundaries so downstream prompts can cite page numbers. 3. Keep table extraction simple: split rows on tabs or repeated spaces, then let a later schema pass normalize columns