Borrowing it
Nothing to install: this file belongs to TheSmokeDev/taskchad-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TheSmokeDev/taskchad-os/master/.claude/skills/pdf/SKILL.mdgit clone --depth 1 https://github.com/TheSmokeDev/taskchad-osWrote 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/thesmokedev/taskchad-os/pdf)<a href="https://agentmods.dev/skills/thesmokedev/taskchad-os/pdf"><img src="https://agentmods.dev/badge/skills/thesmokedev/taskchad-os/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/thesmokedev/taskchad-os/pdf"><img src="https://agentmods.dev/badge/skills/thesmokedev/taskchad-os/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 | $0.00057 | $0.01148 |
| Opus 5.5 | $0.00023 | $0.00459 |
| Sonnet 5.5 | $0.00011 | $0.00230 |
| Haiku 4.5 | $0.00006 | $0.00115 |
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.
How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Processing
Quick Reference
| Task | Tool | Example |
|---|---|---|
| Extract text | pdfplumber | page.extract_text() |
| Extract tables | pdfplumber | page.extract_tables() |
| Merge PDFs | pypdf | writer.add_page(page) |
| Split PDFs | pypdf | One page per file |
| Create PDFs | reportlab | Canvas or Platypus |
| Convert to images | pypdfium2 | page.render() |
| Fill forms | pypdf | See forms section |
Dependencies
pip install pypdf pdfplumber reportlab pypdfium2
Extract Text
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
for page in pdf.pages:
text = page.extract_text()
print(text)
Extract Tables
import pdfplumber
import pandas as pd
with pdfplumber.open("document.pdf") as pdf:
for page in pdf.pages:
tables = page.extract_tables()
for table in tables:
if table:
df = pd.DataFrame(table[1:], columns=table[0])
print(df)
Merge PDFs
from pypdf import PdfWriter, PdfReader
writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf"]:
reader = PdfReader(pdf_file)
for page in reader.pages:
writer.add_page(page)
with open("merged.pdf", "wb") as output:
writer.write(output)
Split PDF
from pypdf import PdfReader, PdfWriter
reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
writer = PdfWriter()
writer.add_page(page)
with open(f"page_{i+1}.pdf", "wb") as output:
writer.write(output)
Create PDF with reportlab
from reportlab.lib.pagesizes import letter
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors
doc = SimpleDocTemplate("output.pdf", pagesize=letter)
styles = getSampleStyleSheet()
story = []
# Add title
story.append(Paragraph("Document Title", styles['Title']))
story.append(Spacer(1, 12))
# Add body text
story.append(Paragraph("This is body text.", styles['Normal']))
# Add table
data = [["Header 1", "Header 2"], ["Cell 1", "Cell 2"]]
table = Table(data)
table.setStyle(TableStyle([
('BACKGROUND', (0, 0), (-1, 0), colors.grey),
('GRID', (0, 0), (-1, -1), 1, colors.black),
]))
story.append(table)
doc.build(story)
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.
- 1mo ago First seen · 199 lines · 57 tokens per session scan A ace22866b11e
pdf is a skill published in the GitHub repository TheSmokeDev/taskchad-os (28 stars, last pushed 23d ago), licensed MIT. It adds 57 tokens to every session and 1,148 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.
Other skills, from other repositories
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.
pkf-writing
Author, update, convert, validate, persist, and render PKF (.pkf) content through message-inline, Library Asset, or Memory Page carriers. Use for structured reports, knowledge pages, delivery cards, or any .pkf body. NOT for recall, memory-graph placement, or curation decisions — those belong to the memory skill.
liteparse
Parse local document files into LLM-ready content on the daemon itself — PDFs → Markdown / structured JSON (with bounding boxes) / page screenshots via the bundled lit CLI. PDF works out of the box, offline, zero external deps (Tesseract + PDFium are bundled): digital PDFs use a near-instant native text path…
ingest
Read web pages and search the web through the workspace Load lane. A query or URL returns document outlines with refs; pass refs back to read the original text of the sections you need, batch several refs in one call, and follow nextRef only while more evidence is needed. Use whenever the user gives a URL, asks you to…
ocr-document
Extract text from PDFs, images, and scanned documents. Uses pymupdf (local) or optional cloud OCR APIs.
ov-add-paper
Load when the user asks to add, import, compile, or ingest a research paper/PDF into OpenViking, especially when they mention ov-add-paper, ARA, claims, evidence, figures, tables, or paper-to-OV knowledge resources.