pdf-explore

A tool for exploring a PDF or other long document when the needed information is spread across multiple sections or pages. It keeps parsed page text available so you can search and combine findings without repeatedly rereading the document.

In plain words
What is it for?
Summarizing methods, locating discussions, reading chart labels, extracting tables, and searching an entire paper or report.
Why use it?
It makes large-document research more manageable when you need to compare sections, find repeated topics, read figures, or extract information throughout the file.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/emaballarin/ccplugins/pdf-explore
Any agent
npx skills add emaballarin/ccplugins --skill pdf-explore
Clone the repo
git clone --depth 1 https://github.com/emaballarin/ccplugins

Made for: Claude Code, Codex.

Per session 264 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,517 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00264 $0.05517
Opus 5 $0.00132 $0.02759
Sonnet 5 $0.00053 $0.01103
Haiku 4.5 $0.00026 $0.00552

Measured yesterday against content hash 88fad2b51582, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

pdf-explore scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

The scan reads SKILL.md. This mod also ships 1 executable file (kernel.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/ccscience/skills/pdf-explore/SKILL.md · 420 lines

How it starts

The opening of the file, as written. The whole thing — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PDF Explore — navigate a PDF too big to embed

The built-in Read(file_path=..., pages=[...]) attaches PDF pages as vision blocks — capped at ~20 pages/turn and dropped from context after one turn, so multi-section synthesis turns into re-reading the same pages over and over. And when the answer is "every page" (list all datasets / citations / figures mentioned anywhere), reading the whole document page-by-page is the expensive way to get it.

This skill parses the PDF once into persistent per-page text, and — for whole-document work — runs one cheap model call per page (or per batch) in parallel via Task subagents, so the page text lives in files and subagent contexts, never in yours. You load only what matters, or sweep every page without putting the pages in your own context at all.

Read(pages=...) and this skill are complementary: use Read(pages=...) for a one-off look at 1–4 pages you will quote in your very next reply; use this skill for persistent text, multi-section synthesis, whole-doc sweeps, and structured extraction.

Loading the kernel

The helpers live in kernel.py next to this file. It is not auto-injected — import it by path in a Bash python heredoc (zero import-time side effects, all heavy imports are lazy):

python3 - <<'PY'
import importlib.util
K = "/ABSOLUTE/PATH/TO/pdf-explore/kernel.py"   # this SKILL.md's directory + /kernel.py
spec = importlib.util.spec_from_file_location("pdf_kernel", K)
k = importlib.util.module_from_spec(spec)
spec.loader.exec_module(k)

for e in k.pdf_outline("paper.pdf"):
    print(f"p{e['page']:>3} {'  ' * (e['level'] - 1)}{e['heading']}")
PY

Every recipe below assumes k is loaded this way. Each python invocation is a fresh process — the in-memory page cache does not survive between them, but page renders are cached on disk and re-extracting the text layer is cheap, so re-parsing the same file in a later call is fast.

Invocation. The recipes below all open with python3 - <<'PY', which is right when the deps are importable from your python3. If they aren't — or you would rather not install anything — prefix with uv, which fetches them into a throwaway env:

Read the full file on GitHub · 420 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 420 lines · 264 tokens per session scan A 88fad2b51582

Subscribe to this mod's changes

pdf-explore is a skill published in the GitHub repository emaballarin/ccplugins (3 stars, last pushed 26d ago), licensed MIT. It adds 264 tokens to every session and 5,517 once invoked, about $0.0013 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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