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 swesmith/davila7__claude-code-templates.734b8a50 --skill pdf-processinggit clone --depth 1 https://github.com/swesmith/davila7__claude-code-templates.734b8a50Wrote 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/swesmith/davila7__claude-code-templates.734b8a50/pdf-processing)<a href="https://agentmods.dev/skills/swesmith/davila7__claude-code-templates.734b8a50/pdf-processing"><img src="https://agentmods.dev/badge/skills/swesmith/davila7__claude-code-templates.734b8a50/pdf-processing/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/swesmith/davila7__claude-code-templates.734b8a50/pdf-processing"><img src="https://agentmods.dev/badge/skills/swesmith/davila7__claude-code-templates.734b8a50/pdf-processing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00036 | $0.00770 |
| Opus 5.5 | $0.00014 | $0.00308 |
| Sonnet 5.5 | $0.00007 | $0.00154 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
PDF Processing 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.
This is a copy
100% identical to pdf-processing — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Processing
Quick start
Use pdfplumber to extract text from PDFs:
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
text = pdf.pages[0].extract_text()
print(text)
Extracting tables
Extract tables from PDFs with automatic detection:
import pdfplumber
with pdfplumber.open("report.pdf") as pdf:
page = pdf.pages[0]
tables = page.extract_tables()
for table in tables:
for row in table:
print(row)
Extracting all pages
Process multi-page documents efficiently:
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
full_text = ""
for page in pdf.pages:
full_text += page.extract_text() + "\n\n"
print(full_text)
Form filling
For PDF form filling, see FORMS.md for the complete guide including field analysis and validation.
Merging PDFs
Combine multiple PDF files:
from pypdf import PdfMerger
merger = PdfMerger()
for pdf in ["file1.pdf", "file2.pdf", "file3.pdf"]:
merger.append(pdf)
merger.write("merged.pdf")
merger.close()
Splitting PDFs
Extract specific pages or ranges:
from pypdf import PdfReader, PdfWriter
reader = PdfReader("input.pdf")
writer = PdfWriter()
# Extract pages 2-5
for page_num in range(1, 5):
writer.add_page(reader.pages[page_num])
with open("output.pdf", "wb") as output:
writer.write(output)
Available packages
- pdfplumber - Text and table extraction (recommended)
- pypdf - PDF manipulation, merging, splitting
- pdf2image - Convert PDFs to images (requires poppler)
- pytesseract - OCR for scanned PDFs (requires tesseract)
Common patterns
Extract and save text:
import pdfplumber
with pdfplumber.open("input.pdf") as pdf:
text = "\n\n".join(page.extract_text() for page in pdf.pages)
with open("output.txt", "w") as f:
f.write(text)
Extract tables to CSV:
import pdfplumber
import csv
with pdfplumber.open("tables.pdf") as pdf:
tables = pdf.pages[0].extract_tables()
with open("output.csv", "w", newline="") as f:
writer = csv.writer(f)
for table in tables:
writer.writerows(table)
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 · 150 lines · 36 tokens per session scan A 1bbb21910cec
PDF Processing is a skill published in the GitHub repository swesmith/davila7__claude-code-templates.734b8a50 (2 stars, last pushed 10mo ago), licensed MIT. It adds 36 tokens to every session and 770 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to pdf-processing, differing in 2 lines, and is treated as a copy.
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