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.
npx skills add EldanRing/qubicl --skill pdfgit clone --depth 1 https://github.com/EldanRing/qubiclWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/eldanring/qubicl/pdf)<a href="https://agentmods.dev/skills/eldanring/qubicl/pdf"><img src="https://agentmods.dev/badge/skills/eldanring/qubicl/pdf/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/eldanring/qubicl/pdf"><img src="https://agentmods.dev/badge/skills/eldanring/qubicl/pdf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.00387 |
| Opus 5.5 | $0.00018 | $0.00155 |
| Sonnet 5.5 | $0.00009 | $0.00077 |
| Haiku 4.5 | $0.00005 | $0.00039 |
Grade A, and why
pdf 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 1mo ago.
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.
What it actually says
PDF workflows
Use Qubicl file tools to inspect inputs and save outputs under /home/qubicl. Use exec_command for the deterministic helpers bundled with this skill. Obtain this package's resourceRoot from skill_view; do not assume a client-specific skill directory.
The curated browser, computer, and workstation images provide the declared Python libraries plus Poppler. Run helpers through /opt/qubicl/skills-venv/bin/python. If a dependency is absent, report that the current preset does not support the operation; do not install packages automatically.
Common helpers beneath <resourceRoot>/scripts/:
pdf_read.py FILE --text|--tables|--meta|--fieldspdf_create.py SPEC.json -o OUTPUT.pdfpdf_merge.py INPUT... -o OUTPUT.pdfpdf_split.py FILE --pages 1-3,5 -o OUTPUT.pdfpdf_page_image.py FILE --out-dir DIRpdf_make_form.py SPEC.json -o OUTPUT.pdfpdf_fill_form.py FILE --fields-json VALUES.json -o OUTPUT.pdfpdf_stamp.py,pdf_watermark.py,pdf_meta.py, andpdf_secure.py
Read references/forms.md only for AcroForm work.
For inspection, extract text first and render representative pages when layout matters. Use take_screenshot only for the live desktop; use generated page images for document QA. Verify important outputs by reopening them with a read helper and, when visual fidelity matters, by rendering pages. Never overwrite the only input copy unless the user explicitly requests in-place editing.
What ships with it
15 files 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.
- LICENSE 1.0 KB
- references/forms.md 3.9 KB
- scripts/_raster.py 2.5 KB runs code
- scripts/pdf_create.py 4.4 KB runs code
- scripts/pdf_fill_form.py 3.8 KB runs code
- scripts/pdf_form_layout.py 7.0 KB runs code
- scripts/pdf_make_form.py 5.8 KB runs code
- scripts/pdf_merge.py 1.6 KB runs code
- scripts/pdf_meta.py 4.3 KB runs code
- scripts/pdf_page_image.py 3.5 KB runs code
- scripts/pdf_read.py 5.7 KB runs code
- scripts/pdf_secure.py 2.6 KB runs code
- scripts/pdf_split.py 3.0 KB runs code
- scripts/pdf_stamp.py 5.6 KB runs code
- scripts/pdf_watermark.py 1.8 KB runs code
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.
- 1mo ago First seen · 26 lines · 45 tokens per session scan A 320e0cb13f88
pdf is a skill published in the GitHub repository EldanRing/qubicl (13 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 45 tokens to every session and 387 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
Manipulate PDF files — merge, split, extract pages/text, PDF↔images, OCR, info — via the qpdf / poppler / ocrmypdf CLIs. Use to combine, slice, convert, or OCR PDFs.
pandoc
Convert documents between formats (Markdown, DOCX, HTML, PDF, RST, EPUB, LaTeX) via the pandoc CLI. Use to transform a document from one format to another.
pkf-writing
Author, update, convert, validate, persist, and render PKF (.pkf) content through message-inline, Library Asset, or Memory Page carriers. Use for structured reports, knowledge pages, delivery cards, or any .pkf body. NOT for recall, memory-graph placement, or curation decisions — those belong to the memory skill.
liteparse
Parse local document files into LLM-ready content on the daemon itself — PDFs → Markdown / structured JSON (with bounding boxes) / page screenshots via the bundled lit CLI. PDF works out of the box, offline, zero external deps (Tesseract + PDFium are bundled): digital PDFs use a near-instant native text path…
ingest
Turn external web URLs into LLM-ready content — load + cache web pages (HQCC compression) and search the web. Use whenever the user gives a URL, asks you to read a webpage, or wants top-K pages on a topic. Executes via the cloud load and cloud search CLIs. For local document parsing (PDF text + OCR), use the liteparse…
pdf-storybook
Turn a dense PDF or document into an illustrated, partly animated scrollable web story in Live Canvas, using generateimage, generatevideo and canvas assets.