alterlab-pdf-explore

name	alterlab-pdf-explore
description	Explore a single PDF in depth — parse it once, then answer questions across its sections, figures, tables, and appendices — comparing methods across sections, extracting every instance of a pattern within the document, and reading values off its charts and tables. Use when interrogating one paper or report end-to-end, pulling every occurrence of something inside a document, or reading data from a figure/table in a PDF, serving the literature-review and paper-review pipeline. To build a comparison table across MANY papers prefer alterlab-pdf-extract; to simply convert a PDF to Markdown prefer alterlab-markitdown; for reference/citation management prefer alterlab-pyzotero. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Write Edit Bash(python:*) Bash(uv:*)
compatibility	Runs under `uv run python` with a PDF parser (e.g. `pymupdf`/`pdfplumber` — TODO(verify) preferred pin) plus, for scanned pages, an OCR pass. No GPU or account required. Parses the PDF once into a section/figure/table index that later questions reuse; large scanned PDFs are slower due to OCR.
metadata	{"skill-author":"AlterLab","version":"1.0.0"}

PDF Explore (deep single-document Q&A)

Overview

Reading one paper or report thoroughly means jumping between the methods, a results figure, a supplementary table, and an appendix — repeatedly. This skill parses a PDF once into a reusable index of sections, figures, and tables, then answers many questions across the whole document without re-parsing: compare what two sections say, extract every instance of a pattern, and read values off charts/tables.

It is the single-document deep-dive counterpart to the corpus-level tools: to build a comparison table across many papers use alterlab-pdf-extract; to just convert a PDF to Markdown use alterlab-markitdown.

When to Use This Skill

Use this skill when the user wants to:

• Interrogate one paper/report end-to-end (methods vs. results vs. appendix).
• Extract every occurrence of a pattern within a document (all p-values, all sample sizes, every mention of a gene).
• Read data off a figure or table inside the PDF.
• Feed precise, located answers into the literature-review or paper-review pipeline.

Does NOT Trigger

Scenario	Use instead
Build a comparison table across many papers (one row per paper)	alterlab-pdf-extract
Convert a PDF/DOCX to clean Markdown	alterlab-markitdown
Manage references / DOIs / BibTeX	alterlab-pyzotero
Work with a Jupyter notebook	alterlab-open-notebook

Core Capabilities

1. Parse-once, ask-many

Parse the PDF a single time into a structured index — page text with layout, detected section headings, figure/table regions, and (for scans) OCR'd text — then answer subsequent questions against that index instead of re-reading the file each time.

2. Cross-section questions

Answer questions that span the document: "does the discussion's claim match the methods' design?", "which limitations does the appendix add beyond the main text?". Cite the page and section for every answer so it is verifiable.

3. Extract-every-instance

Pull all occurrences of a pattern (every reported p-value, every cohort size, every figure that mentions a variable), returning a located list rather than a single hit — the reliable way to avoid missing one.

4. Read values off figures/tables

Extract tabular data from table regions, and read approximate values from a chart (axis ranges, series values) with an explicit note that chart-read values are approximate and should be confirmed against any underlying data.

5. Pipeline role

Provides the per-document evidence that alterlab-paper-reviewer and literature-review workflows build on; hand its located extractions to alterlab-pdf-extract when the task grows from one paper to a whole corpus table.

Resources

• references/pdf_explore_usage.md — parser choice, the section/figure index, OCR handling, extract-every-instance patterns, and figure/table reading caveats. Loaded on demand.

Part of the AlterLab Academic Skills suite.