learn-claude-code is an educational project that builds a small Claude Code-like agent harness from scratch, showing how model access is combined with the surrounding code that lets an agent perceive, reason, and act in an environment. It is for people learning to develop real AI agents. The catalogue add-ons support workflows built around this agent harness.
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 shareAI-lab/learn-claude-code --skill pdfgit clone --depth 1 https://github.com/shareAI-lab/learn-claude-codeWrote 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/shareai-lab/learn-claude-code/pdf)<a href="https://agentmods.dev/skills/shareai-lab/learn-claude-code/pdf"><img src="https://agentmods.dev/badge/skills/shareai-lab/learn-claude-code/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/shareai-lab/learn-claude-code/pdf"><img src="https://agentmods.dev/badge/skills/shareai-lab/learn-claude-code/pdf.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00032 | $0.00717 |
| Opus 5.5 | $0.00013 | $0.00287 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (78,715 stars, last pushed 19d ago), licensed MIT. It adds 32 tokens to every session and 717 once invoked, about $0.0001 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.
Other skills, from other repositories
A set of instructions for working with PDF files, which are documents designed to preserve their layout across devices.
surfsense-documents
Make or change a Word document (.docx), a PDF, a PowerPoint deck (.pptx) or an Excel workbook (.xlsx) by writing a Python script that surfsenserenderdocument runs, optionally starting from a source's own Word or PowerPoint file. Load it whenever the user asks for one of these, or for a change to one you made.
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…
surfsense-pdf
Work on PDFs the user has, or PDFs in Studio, without changing them - merge, take out, split, turn or reorder pages, add a watermark, page numbers, a header or a footer, and list or fill a form's fields. Load it whenever the user asks for one of these on a PDF.
pdf-analysis
A PDF analysis method for reading both normal PDFs with selectable text and scanned PDFs made from page images.