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pdf-explore

Use this skill when the user has attached a PDF, paper, report, or other document and the answer needs content from more than one place in it: summarize the methods or any other section, compare sections, find where a topic is discussed, read a value or label off a figure or chart, or find/list/extract every instance of something across the whole document (datasets, benchmarks, citations, figures, table rows, accession numbers — including appendices). Parses the PDF once with a deterministic Python kernel: `pdf_pages` (pages as persistent text, or high-res images), `pdf_outline` (embedded TOC), `pdf_scan` (a lexical pre-filter that narrows a long doc to candidate pages), `pdf_grep` (regex sweep for exhaustive pattern extraction). You read the shortlist and do the relevance / summary / extraction judgment yourself. For PDF creation/manipulation, use reportlab/pypdf directly.

personAuthor: TashanworldhubOpenAPI
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PDF Explore — navigate a PDF too big to embed

A 50-page PDF read in full is ~200K tokens of context. When the answer draws on several sections at once (summarize the methods; compare section 3 and section 5), or when the answer is "every page" (list all the datasets / citations / figures / benchmarks mentioned anywhere in this document), reading the whole thing page-by-page is the expensive way to get it. This skill parses the PDF once into persistent text with a deterministic Python kernel, then lets you narrow — by outline, by lexical scan, by regex — and read only the pages you actually need, reasoning over them yourself. Nothing you read vanishes: it is ordinary text and ordinary files.

Setup (any agent, no API key)

This is a pure skill — kernel.py is deterministic Python and you (the base model) do all the reasoning. There is no host runtime and no LLM API. Load the helpers once per session in a Python cell:

exec(open("skills/claude-science/pdf-explore/kernel.py").read())
# adjust the path to wherever this skill is installed

Nothing auto-loads it outside Claude Science. Then call the helpers directly (no import). If a helper is "not defined", you haven't exec'd kernel.py yet — go back and run the line above.

Dependencies: pip install pypdfium2 pillow (pillow does the PNG encoding for mode="image"; it is not pulled in by the pypdfium2 wheel).

Which helper

| | when | returns | |---|---|---| | pdf_pages(path, pages=[...], mode="text") | you need several pages/sections at the same time — summaries, comparisons, anything where the answer draws on more than one range | [{page, text, n_chars}, ...] — persistent text; write to a file then read it | | pdf_outline(path) | structured doc (paper, report, book) with an embedded TOC | [{page, heading, level}, ...] — the embedded outline, or [] if the PDF has none | | pdf_scan(path, query, top_k) | narrow a long doc to a handful of candidate pages for a query | {hits: [{page, score, matched, text}], n_scanned} — a lexical pre-filter (no LLM); you read the shortlist and judge relevance | | pdf_grep(path, pattern) | exhaustive regex sweep (DOIs, accession ids, every "Table N", emails) | [{page, matches, lines?}, ...] — every match with its page | | pdf_pages(mode="image", dpi=200) | read a small value, axis label, or legend off a figure | [{page, image_path}, ...] — open the PNG with your agent's image tool |

These come from kernel.py — load it via exec once per session (see Setup), then call directly. pdf_resolve(path) normalizes a path (a workspace path or a ~/-expanded path); the helpers call it internally, so path can be either form.

Note: the default backend is pypdfium2 (Google PDFium; permissive Apache-2.0/BSD-3-Clause). PyMuPDF is honored as a fallback if already installed, but it is AGPL-3.0-licensed (commercial licenses available from Artifex): if you embed it in a network-accessible service, AGPL's source-sharing terms apply to that service.

Recipe — pull the sections you need as persistent text (synthesis)

For "summarize the methods" / "compare section 3 and section 5" / anything where the answer draws on several page ranges at once, pull all the pages you need in one python call, write them to a file, then read that file:

wanted = [5, 21,22,23,24,25, 62,63,64, 124,125,126]  # from pdf_outline
with open("sections.txt", "w") as f:
    for p in pdf_pages("paper.pdf", pages=wanted, mode="text"):
        f.write(f"\n── page {p['page']} ──\n{p['text']}")
import os; print(f"wrote {os.path.getsize('sections.txt'):,} bytes")

Then read sections.txt with your agent's file-read tool (in chunks if it's large) and write the answer from that. It's ordinary text — one parse, and you reason over it directly. Don't print() a full chapter into the cell output: most agents spill large cell output to disk and make you re-read it anyway, so writing + reading a file costs the same two steps without the wasted preview. (For a quick look at ≤5 pages, printing is fine.)

Text is ~800 tokens/page vs ~4,000 tokens/page as vision, and you pay it once. Find the page numbers from pdf_outline (below) or the paper's own table of contents first.

Recipe — navigate by outline (try this first)

for e in pdf_outline("report.pdf"):
    print(f"p{e['page']:>3} {'  ' * (e['level'] - 1)}{e['heading']}")
# → then pull the section you want with pdf_pages(pages=[...])

Free and instant when the PDF has an embedded outline (most LaTeX-compiled papers do). pdf_outline reads the embedded TOC only — if the PDF has none it returns []. In that case build the outline yourself: pull the first handful of pages (or a stride sample of a long doc) as text with pdf_pages and pick out the headings by reading them. For a semantic question the outline doesn't obviously answer ("where do they discuss limitations"), fall through to pdf_scan.

Recipe — find the pages relevant to a query

pdf_scan ranks pages by lexical overlap with your query — it is a cheap pre-filter, not a relevance judgment. It narrows a long document to a handful of candidate pages; you then read those pages and decide which actually answer the question.

r = pdf_scan("paper.pdf", query="batch-effect correction methods", top_k=8)
for h in r["hits"]:
    print(f"p{h['page']}  score={h['score']:.2f}  matched={h['matched']}")
print(f"[{r['n_scanned']} pages scanned]")

Then read the shortlist's text and make the final call yourself:

for h in r["hits"]:
    print(f"\n── page {h['page']} ──\n{h['text'][:2000]}")

Keep the pages that genuinely address the query; discard lexical false positives (a page that merely says "batch" in another sense). Because the ranking is lexical, a synonym the query didn't use won't score — so lean on your own reading, broaden top_k if the shortlist looks thin, and if the term is one you can spell out, cross-check with pdf_grep or pdf_outline.

To skim hit pages as images (layout, tables) instead of text, render them and open the PNGs with your agent's image tool — but a full page is too low-res to read small values off a figure; for that use the next recipe.

for p in pdf_pages("paper.pdf", mode="image",
                   pages=[h["page"] for h in r["hits"]], dpi=150):
    print(p["image_path"])   # open each with your image tool

Recipe — read a figure in detail

A full rendered page is often too low-resolution to read small axis labels, legend text, or values off a dense multi-panel figure. Render the page at high DPI, then crop the figure region before viewing it — the crop is both more legible and cheaper (fewer vision tokens than the whole page).

# 1. Find the figure's page (pdf_scan on the caption text, pdf_grep on the
#    figure label, pdf_outline, or you already know it).
# 2. Render that page at dpi=200 — high enough to crop into.
p = pdf_pages("paper.pdf", mode="image", pages=[5], dpi=200)[0]

# 3. Open p["image_path"] with your agent's image tool to locate the
#    figure, then crop it yourself with pillow before a close read:
from PIL import Image
Image.open(p["image_path"]).crop((x0, y0, x1, y1)).save("fig_crop.png")
#    (x0,y0,x1,y1) are pixels in the dpi=200 render.
# 4. Open fig_crop.png with your image tool.

Crop to one panel at a time for multi-panel figures. Always crop from the full-resolution render on disk, not from an already-downsampled view.

Recipe — list / extract every instance of X across the doc

Two shapes, depending on whether X has a pattern.

Pattern-shaped X (DOIs, accession numbers, "Figure N", emails, anything you can write a regex for) → pdf_grep, an exhaustive regex sweep over the parsed text:

hits = pdf_grep("paper.pdf", r"10\.\d{4,9}/[-._;()/:A-Za-z0-9]+")  # DOIs
for h in hits:
    print(f"p{h['page']}: {h['matches']}")
dois = sorted({m for h in hits for m in h["matches"]})

Returns [{page, matches, lines?}] — every match with its page, so you can build a page-indexed list. Exhaustive by construction (regex over every page) and free.

Judgment-shaped X (datasets results are actually reported on, key claims, figure captions, table rows) — no regex captures it, so you read and extract. Narrow first if you can (pdf_outline to the results section, or pdf_scan / pdf_grep on a likely term), then pull those pages as text and read them:

# e.g. every dataset RESULTS are reported on (not merely cited)
cand = {h["page"] for h in
        pdf_scan("paper.pdf", query="dataset benchmark evaluated", top_k=20)["hits"]}
with open("cand.txt", "w") as f:
    for p in pdf_pages("paper.pdf", pages=sorted(cand)):
        f.write(f"\n── page {p['page']} ──\n{p['text']}")

Read cand.txt and write the list yourself, applying the inclusion criterion ("reported on, not merely cited") as you read — that judgment is now just your own reading. If the document is short, skip the narrowing: pull every page's text into one file and read it straight through — recall-complete, with no filter that could miss anything. De-dupe and normalize as you write the final answer — you have the whole candidate list in context, so collapse "Dataset A" / "DatasetA" / "the A dataset" yourself.

The parse is cached (see Caching), so pulling a few more doubtful pages in a follow-up call is instant and free. Decide all your doubts up front and fetch them in one pdf_pages call rather than dribbling out one-page-at-a-time reads.

When NOT to use this skill

  • A single lookup of 1–4 pages you'll quote immediately: your agent's own PDF/page read tool is fine.
  • Literal keyword / pattern search: use pdf_grep, or filter the extracted text directly — [p for p in pdf_pages(path) if "Harmony" in p["text"]]. pdf_scan earns its keep on multi-word queries where you want a ranked shortlist to read, not on a single exact term.

Mode (scanned PDFs)

All helpers default to mode="auto": try text extraction; if pages average < 80 extractable characters (scanned document, image-only slide export), re-parse with page rendering so you can read the image. You don't need to set this. "text" / "image" force one or the other. Note that pdf_scan and pdf_grep operate on whatever text layer exists — a pure-image scan has none, so for those docs render the pages (mode="image") and read them yourself.

Cost & budget

Text is ~5× fewer tokens per page than a rendered image and it persists, so the winning pattern is: parse once, narrow (outline → scan → grep), and read only the pages you land on. For a very large document you can scan a subset via pages=range(1, n, 3), but stride sampling can miss a narrow relevant span between unrelated neighbors; prefer pdf_outline → read the section you want when the document has structure.

Caching

pdf_pages caches on (abs_path, mtime, mode, dpi) — a second pdf_scan / pdf_grep / pdf_pages with different arguments on the same file skips re-parsing and re-rendering. Pass cache=False to force a fresh parse. Page renders land in ./.cache/pdf-explore/{sha8}-{mtime}/dpi{N}/p{NNN}.png — copy or point your image tool at the ones you want to view.