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
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.
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.
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.
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.
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
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.
1---2name: pdf-reading3description: 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”.4---5
6## Goal
7
8When you need facts from a paper PDF (counts, percentages, benchmark numbers, claims, limitations), extract *verbatim* evidence from the PDF and compute derived values yourself.
9
10This repository’s content often depends on exact values from tables/figures (not abstracts). Always bias toward **precision and traceability**.
11
12## Process
13
141. **Locate the PDF**
15 - Search the repo for `.pdf` files.
16 - If a paper directory contains a source PDF, prefer that.
17 - If the only PDF is in `tmp/` or the repo root, confirm it corresponds to the paper in question before using it.
18
192. **Extract text locally (no network fetches)**
20 - Prefer a local text extraction flow:
21 - Use `.github/skills/pdf-reading/extract_pdf_text.py` to create a plain-text copy in `tmp/`.
22 - If extraction fails, try a different backend (`pypdf` vs `pdftotext`) or fall back to manual inspection.
23
243. **Search within the extracted text**
25 - Use targeted queries first (unique phrases, table titles, “Table 2”, “Appendix”, metric names).
26 - For numbers, search patterns like `n=`, `N=`, `(`, `%)`, `Table`, `Figure`.
27
284. **Verify statistics (repo requirement)**
29 - Prefer raw counts (e.g., “31/50”) over percentages when available.
30 - If the paper gives counts, compute percentages yourself: $\text{pct} = 100 \times \frac{\text{numerator}}{\text{denominator}}$.
31 - If a value is ambiguous (multiple similar tables/ablations), capture the surrounding label/context.
32
335. **Handle common PDF pitfalls**
34 - **Hyphenation and line breaks:** words may be split across lines; search both with and without hyphens.
35 - **Tables:** extracted text may be messy; search by row/column headers and unique tokens.
36 - **Scanned PDFs:** text extraction may fail; use manual reading if needed.
37
38## Output expectations
39
40- When updating a question/paper, report the exact extracted phrase/value and where it came from (section/table/figure name).
41- If you cannot reliably extract the needed value, explicitly say so and propose next steps (e.g., manual verification).
42
43## Commands
44
45- Extract text:
46 - `python3 .github/skills/pdf-reading/extract_pdf_text.py path/to/paper.pdf`
47
48- Extract to a specific file:
49 - `python3 .github/skills/pdf-reading/extract_pdf_text.py path/to/paper.pdf --out tmp/paper.txt`
50
51## Repository conventions to respect
52
53- Keep diffs minimal and consistent with existing patterns.
54- Park derived artifacts under `tmp/` (gitignored).
55- Don’t add new dependencies unless explicitly requested; prefer optional tooling or clear fallbacks.
majiayu000