nano-pdf
Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
- License 许可
- MIT
- 所属仓库
- zapabob/Skills
- 最近更新
- 2026-10-26
- Stars
- ★ 2
- Forks
- 0
01 前言 Front Matter
- name
- nano-pdf
- author
- community
- license
- MIT
- version
- 1.0.0
- metadata
- { "hermes": { "tags": [ "PDF", "Documents", "Editing", "NLP", "Productivity" ], "homepage": "https://pypi.org/project/nano-pdf/", "related_skills": [ "pdf", "ocr-and-documents" ] } }
- platforms
- [ "linux", "macos", "windows" ]
- description
- Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
03 SKILL.md 正文
nano-pdf
Edit PDFs using natural-language instructions. Point it at a page and describe what to change. For structural PDF work (merge, split, forms, watermarks, creation), see the pdf skill; for text extraction from scans, see ocr-and-documents.
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_fileto check file size, or open it) - The tool uses an LLM under the hood — requires an API key (check
nano-pdf --helpfor config) - Works well for text changes; complex layout modifications may need a different approach
04 同仓库更多 Skills
xurl
X/Twitter via xurl CLI: post, search, DM, media, v2 API.
writing-plans
Write implementation plans: bite-sized tasks, paths, code.
whisper
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
weights-and-biases
W&B: log ML experiments, sweeps, model registry, dashboards.
watchers
Poll RSS, JSON APIs, and GitHub with watermark dedup.
serving-llms-vllm
vLLM: high-throughput LLM serving, OpenAI API, quantization.