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 Ertinox7711/SGRR-AGI-V2 --skill pdf-readinggit clone --depth 1 https://github.com/Ertinox7711/SGRR-AGI-V2Wrote 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/ertinox7711/sgrr-agi-v2/pdf-reading)<a href="https://agentmods.dev/skills/ertinox7711/sgrr-agi-v2/pdf-reading"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/pdf-reading/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/ertinox7711/sgrr-agi-v2/pdf-reading"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/pdf-reading.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 | $0.00115 | $0.02738 |
| Opus 5.5 | $0.00046 | $0.01095 |
| Sonnet 5.5 | $0.00023 | $0.00548 |
| Haiku 4.5 | $0.00012 | $0.00274 |
Grade A, and why
pdf-reading 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-reading — 0 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 — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Processing Guide
Overview
This guide covers essential PDF reading operations using Python libraries and command-line tools. For advanced features (pypdfium2 rendering, pdfplumber table settings, OCR fallback, encrypted/corrupted PDF handling), see REFERENCE.md.
Reading & Inspecting PDFs
Before doing anything with a PDF, understand what you're working with.
Content inventory
Run a quick diagnostic first. For simple tasks ("summarize this
document"), pdfinfo + pdffonts + a text sample may suffice. For
anything involving figures, attachments, or extraction issues, run the
full set:
# Always: page count, file size, PDF version, metadata
pdfinfo document.pdf
# Always: does a text layer exist? No fonts → scanned/raster → see "Scanned documents"
pdffonts document.pdf
# If fonts are present: sample the text layer
pdftotext -f 1 -l 1 document.pdf - | head -20
# If figures/charts may matter:
pdfimages -list document.pdf
# If the PDF might contain embedded files (reports, portfolios):
pdfdetach -list document.pdf
This tells you:
- Page count and size — how big is the job?
- Font status — are any fonts present? An empty
pdffontstable means the PDF is scanned or raster-only:pdftotextwill return nothing, so skip straight to "Scanned documents" below. Fonts shown as not embedded ("emb: no") with custom encodings may produce wrong characters on extraction. - Text extractability — when fonts exist, does
pdftotextreturn clean text, or is it garbled (broken encoding)? - Embedded raster images — are there photos or raster figures? (Note: vector-drawn charts from matplotlib/Excel won't appear — see "Extracting embedded images" below)
- Attachments — are there embedded spreadsheets, data files, etc.?
Text extraction
pypdf for basic text:
from pypdf import PdfReader
reader = PdfReader("document.pdf")
print(f"Pages: {len(reader.pages)}")
# Extract text
text = ""
for page in reader.pages:
text += page.extract_text()
What ships with it
2 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 · 309 lines · 115 tokens per session scan A e2081f071b4f
pdf-reading is a skill published in the GitHub repository Ertinox7711/SGRR-AGI-V2 (1 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 2,738 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to pdf-reading, differing in 0 lines, and is treated as a copy.
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