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 gabrielmoreira/agent-skills-mirror --skill pdfgit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/pdf)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/pdf"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/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/gabrielmoreira/agent-skills-mirror/pdf"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/pdf.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 | $0.00092 | $0.06298 |
| Opus 5 | $0.00046 | $0.03149 |
| Sonnet 5 | $0.00018 | $0.01260 |
| Haiku 4.5 | $0.00009 | $0.00630 |
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 9d 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 — 616 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Processing Guide
Overview
This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see REFERENCE.md. If you need to fill out a PDF form, read FORMS.md and follow its instructions.
Default Output Format for Complex Tasks
When a user's task involves multiple tool calls (web search, data analysis, tool invocations, etc.) and produces substantial research results, but the user has NOT specified an output format, you should default to generating a PDF report using the template below. This applies to:
- Research tasks that gather information from multiple sources
- Analysis tasks that produce structured findings
- Any multi-step task where the final deliverable is a comprehensive answer
Do NOT default to PDF for simple Q&A, quick lookups, or tasks where the user clearly expects a chat response. Use your judgment: if the task took 5+ tool calls and produced rich, structured content, a PDF report is the appropriate default.
When defaulting to PDF output, follow the "Generate PDF Reports" workflow below — use generate_report.py with report_data.json, NOT the markdown-to-PDF approach.
Quick Start — Choosing the Right Tool for Text Extraction
Not all extractors are equal. Pick the right one based on your PDF type:
| PDF Type | Best Tool | Why |
|---|---|---|
| Academic papers (two-column, conference/journal) | pdftotext -layout (poppler) |
Handles column detection and character spacing reconstruction |
| Simple single-column documents | pdfplumber or pypdf | Good enough, easier to script |
| Scanned PDFs (image-based) | pytesseract + pdf2image | Needs OCR |
| Tables / structured data | pdfplumber | Best table extraction |
Academic Papers — Use pdftotext First
Most academic PDFs (arXiv, IEEE, ACM, etc.) use two-column layouts and custom font encodings where spaces are implicit (encoded as character spacing, not space characters). Python libraries like pypdf and basic pdfplumber often produce merged words (e.g. "TheConferenceonAI" instead of "The Conference on AI").
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.
- 9d ago First seen · 616 lines · 92 tokens per session scan A 1fa584be1c7c
pdf is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 92 tokens to every session and 6,298 once invoked, about $0.0005 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 2026-09-03.
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.