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/shareai-lab/learn-claude-code/pdfnpx skills add shareAI-lab/learn-claude-code --skill pdfgit clone --depth 1 https://github.com/shareAI-lab/learn-claude-codeWhat 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 | $0.00032 | $0.00717 |
| Opus 5 | $0.00016 | $0.00358 |
| Sonnet 5 | $0.00006 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00072 |
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.
Copies of this mod
8 near-identical copies found in the catalogue:
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Processing Skill
You now have expertise in PDF manipulation. Follow these workflows:
Reading PDFs
Option 1: Quick text extraction (preferred)
# Using pdftotext (poppler-utils)
pdftotext input.pdf - # Output to stdout
pdftotext input.pdf output.txt # Output to file
# If pdftotext not available, try:
python3 -c "
import fitz # PyMuPDF
doc = fitz.open('input.pdf')
for page in doc:
print(page.get_text())
"
Option 2: Page-by-page with metadata
import fitz # pip install pymupdf
doc = fitz.open("input.pdf")
print(f"Pages: {len(doc)}")
print(f"Metadata: {doc.metadata}")
for i, page in enumerate(doc):
text = page.get_text()
print(f"--- Page {i+1} ---")
print(text)
Creating PDFs
Option 1: From Markdown (recommended)
# Using pandoc
pandoc input.md -o output.pdf
# With custom styling
pandoc input.md -o output.pdf --pdf-engine=xelatex -V geometry:margin=1in
Option 2: Programmatically
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
c = canvas.Canvas("output.pdf", pagesize=letter)
c.drawString(100, 750, "Hello, PDF!")
c.save()
Option 3: From HTML
# Using wkhtmltopdf
wkhtmltopdf input.html output.pdf
# Or with Python
python3 -c "
import pdfkit
pdfkit.from_file('input.html', 'output.pdf')
"
Merging PDFs
import fitz
result = fitz.open()
for pdf_path in ["file1.pdf", "file2.pdf", "file3.pdf"]:
doc = fitz.open(pdf_path)
result.insert_pdf(doc)
result.save("merged.pdf")
Splitting PDFs
import fitz
doc = fitz.open("input.pdf")
for i in range(len(doc)):
single = fitz.open()
single.insert_pdf(doc, from_page=i, to_page=i)
single.save(f"page_{i+1}.pdf")
Key Libraries
| Task | Library | Install |
|---|---|---|
| Read/Write/Merge | PyMuPDF | pip install pymupdf |
| Create from scratch | ReportLab | pip install reportlab |
| HTML to PDF | pdfkit | pip install pdfkit + wkhtmltopdf |
| Text extraction | pdftotext | brew install poppler / apt install poppler-utils |
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 · 113 lines · 32 tokens per session scan A b143723126a9
pdf is a skill published in the GitHub repository shareAI-lab/learn-claude-code (75,835 stars, last pushed 6d ago), licensed MIT. It adds 32 tokens to every session and 717 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.
Other skills, from other repositories
Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and…
当用户需要对PDF文件进行任何操作时,请使用此技能。包括从 PDF 中读取或提取文本/表格、合并多个 PDF、拆分 PDF、旋转页面、添加水印、创建新PDF、填写PDF表单、加密/解密 PDF、提取图片,以及对扫描版 PDF 进行 OCR 使其可搜索。如果用户提到 .pdf 文件或要求生成 PDF,请使用此技能。.
nano-pdf
Edits PDF files using natural-language instructions via the nano-pdf CLI. Supports modifying text, changing titles, fixing typos, and updating content on specific pages. Use when the user wants to edit a PDF, modify PDF content, update PDF text, fix a typo in a PDF, change a PDF title, or rewrite part of a PDF page.
hive.pdf
Read, write, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python (pypdf, pdfplumber, reportlab, pypdfium2) and command-line tools (poppler-utils, qpdf). Use when the user asks to extract text/tables/images from a PDF, create or modify a PDF, combine or split PDFs, OCR a scanned PDF…
pdf-toolkit
Structured .pdf operations: extract text/tables, merge pages from multiple PDFs, split a PDF by page ranges, fill PDF form fields, and generate fresh PDFs from JSON. Trigger when the user wants programmatic PDF work without natural-language rewriting — examples: pull tables from a report, combine three PDFs, extract…
nano-pdf
Edit PDFs with natural-language instructions using the nano-pdf CLI.