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Extract text from PDF files using Apple Vision OCR, optimized for Apple Silicon
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Use when the user asks to "browse a website", "go to a URL", "fill out a form", "take a screenshot", "click on something", "extract data from a page", "automate a browser task", "control the desktop", "use the computer", or any task involving web pages or desktop applications.
Use when the user wants to make a phone call, leave a voicemail, call a business, reach someone by phone, or navigate a phone menu. Triggered by "/call" followed by a phone number and purpose.
Start or continue a progressive learning session. Use when the user says "learn", "let's learn", "continue learning", "let's continue", "pick up where we left off", "teach me", "next lesson", or wants to resume their learning curriculum. Requires a phase map to exist in docs/phases/.
Design progressive learning curricula and generate project phase maps. Use when the user says "learn-design", "design curriculum", "plan learning path", "set up next project", "what should I learn next", or wants to start learning a new technical area. Also triggers between projects to generate the next project's phase map calibrated to the learner's profile.
This skill converts any content source into podcast episodes. Triggers: sending a URL, PDF, YouTube link, book, or file with intent to listen; phrases like 'podcast this', 'make this a podcast', 'listen to this', 'generate a podcast'; sending content with instructions like 'split by chapters', 'make a series'. Handles single articles, multi-chapter books, YouTube playlists, and any document format.
This skill should be used when the user asks to "read a paper", "understand a paper", "analyze a research paper", "prepare for paper discussion", "work through a paper", "do a literature review", or provides PDF paths to research papers. Guides deep paper understanding using Keshav's three-pass method with AI as a patient professor. Supports multiple papers for literature review.
| name | pdf-ocr |
| description | Extract text from PDF files using Apple Vision OCR, optimized for Apple Silicon |
Converts PDF pages to images using PyMuPDF, then runs Apple Vision OCR on each page in parallel. Produces Markdown, plain text, or JSONL output.
Use this skill when the user wants to extract text from a PDF file -- especially scanned PDFs, image-based PDFs, or PDFs where copy-paste produces garbled text.
python3 ${CLAUDE_PLUGIN_ROOT}/scripts/pdf_ocr.py <input.pdf> \
[-o output_file] \
[-f markdown|text|jsonl] \
[--dpi 200] \
[--workers N] \
[--languages en-US] \
[--fast] \
[--keep-images] [--images-dir <dir>] \
[--stdout]
| Argument | Default | Description |
|---|---|---|
pdf (positional) | required | Input PDF file path |
-o, --output | <pdf_name>.md | Output file path |
-f, --format | markdown | Output format: markdown, text, or jsonl |
--dpi | 200 | Resolution for rendering PDF pages (higher = better quality, slower) |
--workers | CPU count | Number of parallel OCR workers |
--languages | en-US | Comma-separated recognition languages |
--fast | false | Use faster, less accurate recognition |
--keep-images | false | Keep the extracted page images after OCR |
--images-dir | temp dir | Directory to save page images (requires --keep-images) |
--stdout | false | Print extracted text to stdout instead of writing to file |
## Page N section with the OCR text=== Page N === separatorspage, text, and backend fields