Nano pdf

Skill diamitani/rostr-agent/skills/productivity/nano-pdf

ROSTR Agent — Self-improving multi-agent framework with built-in PAL, NPAO, RAG DAL, and Rostr Hub. Built on Hermes Agent by Nous Research.

Install

npx -y skills add diamitani/rostr-agent --skill nano-pdf

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

1.4 KB, 278 tokens by cl100k_base, as published. Nobody here has run it

nano-pdf

Edit PDFs using natural-language instructions. Point it at a page and describe what to change.

Prerequisites

# Install with uv (recommended — already available in Hermes)
uv pip install nano-pdf

# Or with pip
pip install nano-pdf

Usage

nano-pdf edit <file.pdf> <page_number> "<instruction>"

Examples

# Change a title on page 1
nano-pdf edit deck.pdf 1 "Change the title to 'Q3 Results' and fix the typo in the subtitle"

# Update a date on a specific page
nano-pdf edit report.pdf 3 "Update the date from January to February 2026"

# Fix content
nano-pdf edit contract.pdf 2 "Change the client name from 'Acme Corp' to 'Acme Industries'"

Notes

Page numbers may be 0-based or 1-based depending on version — if the edit hits the wrong page, retry with ±1
Always verify the output PDF after editing (use read_file to check file size, or open it)
The tool uses an LLM under the hood — requires an API key (check nano-pdf --help for config)
Works well for text changes; complex layout modifications may need a different approach

Gives 0 of the 12 instructions most pdf office docs skills give in 278 tokens

Counted across 636 of the 690 authors here whose files we hold, read 2026-08-07

extract text using pdfplumberin 89 of 636, across 23 files
create PDFs using reportlabin 83 of 636, across 16 files
read forms.md to fill out pdf formsin 80 of 636, across 13 files
OCR scanned PDFs using pytesseractin 77 of 636, across 10 files
merge or split PDFs using qpdfin 70 of 636, across 3 files
use excel formulas instead of hardcoded calculated valuesin 68 of 636, across 13 files
unpack edit xml and repack existing documentsin 63 of 636, across 8 files
document sources for hardcoded valuesin 61 of 636, across 9 files
write minimal python code without unnecessary commentsin 59 of 636, across 7 files
run the recalculation script after adding or modifying formulasin 59 of 636, across 7 files
fix all identified formula errors and recalculatein 58 of 636, across 6 files
format years as text stringsin 57 of 636, across 5 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.