PDF Text Extract

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:

Extract the text and rough tables from this PDF so Gemini can summarize it.

Output:

# 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.
4. Cap text sent to an LLM by page/range when the PDF is long.
5. Fail loudly when no text is found instead of sending an empty prompt downstream.

Reference: assets/extract.py.

Source

Distilled from production use across the author's automation projects. v1.0.0. See also: [[gemini-structured-output]], [[csv-report-writer]], [[s3-uploader]].

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