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pdf-reading

@pondevelopment/llm-training
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Read local PDFs to extract and verify exact numbers (counts, percentages, tables, figure captions) for papers/questions in this repository. Use this when asked to “read a PDF”, “extract results from the paper”, “verify a statistic”, or “find the exact wording in the paper”.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name pdf-reading
description Read local PDFs to extract and verify exact numbers (counts, percentages, tables, figure captions) for papers/questions in this repository. Use this when asked to “read a PDF”, “extract results from the paper”, “verify a statistic”, or “find the exact wording in the paper”.

Goal

When you need facts from a paper PDF (counts, percentages, benchmark numbers, claims, limitations), extract verbatim evidence from the PDF and compute derived values yourself.

This repository’s content often depends on exact values from tables/figures (not abstracts). Always bias toward precision and traceability.

Process

  1. Locate the PDF

    • Search the repo for .pdf files.
    • If a paper directory contains a source PDF, prefer that.
    • If the only PDF is in tmp/ or the repo root, confirm it corresponds to the paper in question before using it.

  2. Extract text locally (no network fetches)

    • Prefer a local text extraction flow:
      • Use .github/skills/pdf-reading/extract_pdf_text.py to create a plain-text copy in tmp/.
      • If extraction fails, try a different backend (pypdf vs pdftotext) or fall back to manual inspection.

  3. Search within the extracted text

    • Use targeted queries first (unique phrases, table titles, “Table 2”, “Appendix”, metric names).
    • For numbers, search patterns like n=, N=, (, %), Table, Figure.

  4. Verify statistics (repo requirement)

    • Prefer raw counts (e.g., “31/50”) over percentages when available.
    • If the paper gives counts, compute percentages yourself: $\text{pct} = 100 \times \frac{\text{numerator}}{\text{denominator}}$.
    • If a value is ambiguous (multiple similar tables/ablations), capture the surrounding label/context.

  5. Handle common PDF pitfalls

    • Hyphenation and line breaks: words may be split across lines; search both with and without hyphens.
    • Tables: extracted text may be messy; search by row/column headers and unique tokens.
    • Scanned PDFs: text extraction may fail; use manual reading if needed.

Output expectations

  • When updating a question/paper, report the exact extracted phrase/value and where it came from (section/table/figure name).
  • If you cannot reliably extract the needed value, explicitly say so and propose next steps (e.g., manual verification).

Commands

  • Extract text:

    • python3 .github/skills/pdf-reading/extract_pdf_text.py path/to/paper.pdf

  • Extract to a specific file:

    • python3 .github/skills/pdf-reading/extract_pdf_text.py path/to/paper.pdf --out tmp/paper.txt

Repository conventions to respect

  • Keep diffs minimal and consistent with existing patterns.
  • Park derived artifacts under tmp/ (gitignored).
  • Don’t add new dependencies unless explicitly requested; prefer optional tooling or clear fallbacks.