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 sinaptik-ai/starpod --skill pdfgit clone --depth 1 https://github.com/sinaptik-ai/starpodWrote 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/sinaptik-ai/starpod/pdf)<a href="https://agentmods.dev/skills/sinaptik-ai/starpod/pdf"><img src="https://agentmods.dev/badge/skills/sinaptik-ai/starpod/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/sinaptik-ai/starpod/pdf"><img src="https://agentmods.dev/badge/skills/sinaptik-ai/starpod/pdf.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 | $0.00066 | $0.01712 |
| Opus 5.5 | $0.00026 | $0.00685 |
| Sonnet 5.5 | $0.00013 | $0.00342 |
| Haiku 4.5 | $0.00007 | $0.00171 |
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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Processing
Quick Reference
| Task | Tool | Script |
|---|---|---|
| Extract text | pdfplumber | page.extract_text() |
| Extract tables | pdfplumber | python scripts/extract_tables.py input.pdf |
| Merge PDFs | pypdf | python scripts/merge.py output.pdf a.pdf b.pdf |
| Convert to images | poppler-utils | python scripts/convert_to_images.py input.pdf |
| Fill form fields | pypdf | python scripts/fill_form.py form.pdf --list |
| Create new PDF | reportlab | SimpleDocTemplate or Canvas |
| OCR scanned PDF | pytesseract + pdf2image | image_to_string() |
| CLI text extraction | poppler-utils | pdftotext |
| CLI merge/split | qpdf | qpdf --empty --pages |
For form filling workflow, see references/forms.md.
For advanced libraries (pypdfium2, pdf-lib), see references/advanced.md.
Reading & Extracting
Text extraction
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
for page in pdf.pages:
print(page.extract_text())
Table extraction → DataFrame
import pdfplumber, pandas as pd
with pdfplumber.open("document.pdf") as pdf:
tables = []
for page in pdf.pages:
for table in page.extract_tables():
if table:
df = pd.DataFrame(table[1:], columns=table[0])
tables.append(df)
if tables:
combined = pd.concat(tables, ignore_index=True)
combined.to_excel("tables.xlsx", index=False)
Metadata
from pypdf import PdfReader
reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"Title: {meta.title}, Author: {meta.author}, Pages: {len(reader.pages)}")
Merging & Splitting
Merge multiple PDFs
from pypdf import PdfWriter, PdfReader
writer = PdfWriter()
for path in ["a.pdf", "b.pdf", "c.pdf"]:
for page in PdfReader(path).pages:
writer.add_page(page)
with open("merged.pdf", "wb") as f:
writer.write(f)
Split into individual pages
from pypdf import PdfReader, PdfWriter
for i, page in enumerate(PdfReader("input.pdf").pages):
w = PdfWriter()
w.add_page(page)
with open(f"page_{i+1}.pdf", "wb") as f:
w.write(f)
What ships with it
6 files 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 · 222 lines · 66 tokens per session scan A 8753faffa85d
pdf is a skill published in the GitHub repository sinaptik-ai/starpod (79 stars, last pushed 6mo ago), licensed MIT. It adds 66 tokens to every session and 1,712 once invoked, about $0.0003 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.
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