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 ag2ai/ag2-assistant --skill pdf-toolsgit clone --depth 1 https://github.com/ag2ai/ag2-assistantWrote 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/ag2ai/ag2-assistant/pdf-tools)<a href="https://agentmods.dev/skills/ag2ai/ag2-assistant/pdf-tools"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-assistant/pdf-tools/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/ag2ai/ag2-assistant/pdf-tools"><img src="https://agentmods.dev/badge/skills/ag2ai/ag2-assistant/pdf-tools.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.00496 |
| Opus 5.5 | $0.00021 | $0.00198 |
| Sonnet 5.5 | $0.00010 | $0.00099 |
| Haiku 4.5 | $0.00005 | $0.00050 |
Grade A, and why
pdf-tools 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 18d 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
Working with PDFs
Reading a PDF
- To understand or summarise a PDF the user points you at, use the
read_filetool with its path. AG2 Assistant hands PDFs to the model as visual content, so this works even for scanned PDFs with no text layer (forms, signed documents, image scans). Ask permission the first time if prompted. - For a born-digital PDF where you need the exact text (to quote precisely, count, or post-process), extract the text via code execution instead (below).
Manipulating a PDF (code execution)
Use the code-execution tool. Prefer pypdf (pure-Python, no system deps); fall
back to pdfplumber for tables. Install on first use if missing
(pip install pypdf).
- Extract text:
from pypdf import PdfReader r = PdfReader("in.pdf") print("\n".join(page.extract_text() or "" for page in r.pages)) - Split / select pages:
from pypdf import PdfReader, PdfWriter r = PdfReader("in.pdf") w = PdfWriter() for i in (0, 1, 2): # first three pages w.add_page(r.pages[i]) with open("out.pdf", "wb") as f: w.write(f) - Merge: add pages from several
PdfReaders into onePdfWriter. - Tables:
pdfplumber→page.extract_tables()is more reliable than plain text extraction for tabular data.
Tips
- If text extraction returns empty strings, the PDF is scanned/image-only — read
it visually with
read_fileinstead. - Always tell the user where you wrote any output file.
- Don't fabricate content you couldn't extract — say what failed and ask how to proceed.
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.
- 18d ago Changed · +4 lines e44b5325ce1c
- 1mo ago First seen · 50 lines · 52 tokens per session scan A 20b5384c3b0b
pdf-tools is a skill published in the GitHub repository ag2ai/ag2-assistant (14 stars, last pushed today), licensed Apache-2.0. It adds 52 tokens to every session and 496 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 2026-08-30.
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