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 agentmods add skills/team-commonly/commonly/pdfnpx skills add Team-Commonly/commonly --skill pdfgit clone --depth 1 https://github.com/Team-Commonly/commonlyWhat 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 | $0.00049 | $0.00666 |
| Opus 5 | $0.00024 | $0.00333 |
| Sonnet 5 | $0.00010 | $0.00133 |
| Haiku 4.5 | $0.00005 | $0.00067 |
Grade A, and why
pdf scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
pdf — PDF manipulation toolkit
The gateway image ships:
pdftotext,pdftoppm,pdfinfofrompoppler-utils(apt)pypdf(Python) for programmatic merge / split / metadata edits
Common operations
Extract text from a PDF
pdftotext /workspace/$(basename "$PWD")/input.pdf /workspace/$(basename "$PWD")/input.txt
# Or stream to stdout:
pdftotext /workspace/$(basename "$PWD")/input.pdf -
For LLM-friendly output, prefer the markdown-converter skill (which uses
markitdown and handles tables better). Use pdftotext only when you need
raw text quickly.
Count pages
pdfinfo /workspace/$(basename "$PWD")/input.pdf | grep Pages
Render a page to PNG (preview / OCR input)
# All pages → PNG at 150dpi
pdftoppm -png -r 150 /workspace/$(basename "$PWD")/input.pdf /workspace/$(basename "$PWD")/page
# Just page 1
pdftoppm -png -r 150 -f 1 -l 1 /workspace/$(basename "$PWD")/input.pdf /workspace/$(basename "$PWD")/cover
Merge multiple PDFs
python3 - <<'PY'
from pypdf import PdfWriter
w = PdfWriter()
for f in ["a.pdf", "b.pdf", "c.pdf"]:
w.append(f)
w.write("/workspace/$(basename "$PWD")/merged.pdf")
PY
Split a PDF into per-page files
python3 - <<'PY'
from pypdf import PdfReader, PdfWriter
r = PdfReader("/workspace/$(basename "$PWD")/input.pdf")
for i, page in enumerate(r.pages):
w = PdfWriter()
w.add_page(page)
w.write(f"/workspace/$(basename "$PWD")/page_{i+1}.pdf")
PY
Edit PDF metadata
python3 - <<'PY'
from pypdf import PdfReader, PdfWriter
r = PdfReader("/workspace/$(basename "$PWD")/input.pdf")
w = PdfWriter(clone_from=r)
w.add_metadata({"/Title": "New Title", "/Author": "Theo"})
w.write("/workspace/$(basename "$PWD")/output.pdf")
PY
When NOT to use this skill
- To read a PDF for context — use
markdown-converter(cleaner output). - To create a PDF from markdown — use
pandic-office(pandoc). - To create or edit Office docs (DOCX/XLSX/PPTX) — use
officecli.
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.
- 2d ago First seen · 84 lines · 49 tokens per session scan A 7ac5a9ab76fd
pdf is a skill published in the GitHub repository Team-Commonly/commonly (1,323 stars, last pushed 2d ago), licensed Apache-2.0. It adds 49 tokens to every session and 666 once invoked, about $0.0002 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 2026-08-30.
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