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 vladmdgolam/agent-skills --skill pdf-look-scannedgit clone --depth 1 https://github.com/vladmdgolam/agent-skillsWrote 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/vladmdgolam/agent-skills/pdf-look-scanned)<a href="https://agentmods.dev/skills/vladmdgolam/agent-skills/pdf-look-scanned"><img src="https://agentmods.dev/badge/skills/vladmdgolam/agent-skills/pdf-look-scanned/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/vladmdgolam/agent-skills/pdf-look-scanned"><img src="https://agentmods.dev/badge/skills/vladmdgolam/agent-skills/pdf-look-scanned.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 | $0.00115 | $0.01524 |
| Opus 5 | $0.00057 | $0.00762 |
| Sonnet 5 | $0.00023 | $0.00305 |
| Haiku 4.5 | $0.00012 | $0.00152 |
Grade A, and why
pdf-look-scanned 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 13d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Look Scanned
Make PDFs look like physical scans with optional signature replacement.
Dependencies
pip3 install pillow pdf2image img2pdf numpy
Also requires poppler (provides pdftoppm):
- macOS:
brew install poppler - Ubuntu/Debian:
apt install poppler-utils - Fedora/RHEL:
dnf install poppler-utils - Arch:
pacman -S poppler - Windows:
conda install -c conda-forge poppleror download from poppler releases
Quick Start
Run scripts/make_scanned.py for the core functionality. Read the script's --help for all options.
Simple scan effect
python3 scripts/make_scanned.py document.pdf
Adjust scan quality
Lower DPI and quality = grittier, more realistic cheap-scanner look:
python3 scripts/make_scanned.py document.pdf --dpi 150 --quality 75 --noise 7
Higher DPI = cleaner scan, larger file:
python3 scripts/make_scanned.py document.pdf --dpi 300 --quality 92
Signature Replacement
Finding signature coordinates
Before replacing signatures, determine crop and placement coordinates. All coordinates are fractions of page dimensions (0.0-1.0).
- Convert the source PDF to images and visually inspect to find the signature region
- Iteratively crop to isolate just the signature ink (no text, no table lines)
- On the target PDF, find the area to clear (old digital signature) and where to paste
Use this Python snippet to explore coordinates interactively:
from pdf2image import convert_from_path
from PIL import Image
pages = convert_from_path("document.pdf", dpi=200)
page = pages[0] # 0-indexed
w, h = page.size
# Crop a region and save for inspection
crop = page.crop((int(w*0.5), int(h*0.4), int(w*0.8), int(h*0.6)))
import tempfile, os
crop.save(os.path.join(tempfile.gettempdir(), "crop_test.png"))
Replace one signature
python3 scripts/make_scanned.py contract.pdf \
--sig-pdf signatures.pdf \
--sig-page 1 --sig-crop 0.41,0.33,0.62,0.42 \
--sig-target 25 --sig-clear 0.61,0.41,0.85,0.52 --sig-place 0.62,0.415
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.
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.
- 13d ago First seen · 144 lines · 115 tokens per session scan A b1ae8f5fddc5
pdf-look-scanned is a skill published in the GitHub repository vladmdgolam/agent-skills (8 stars, last pushed 5d ago), licensed MIT. It adds 115 tokens to every session and 1,524 once invoked, about $0.0006 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.
Other skills, from other repositories
pydicom
Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
skill-doc-delivery
Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.
pdf-extract-create-workflow
Complete PDF lifecycle: download, extract, and generate structured documents with reportlab.
document-direct-python
Use direct Python execution for reliable document creation including spreadsheets, PDFs, and structured reports.
parse-document
Convert a PDF, scan, image of a page, or office file to clean markdown through the connected Superlinked MCP edge, so the source document is not read into model context directly. Use when the user asks to read, parse, OCR, extract from, summarize, or answer questions about a document.