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Extract text from a directory of JPG/JPEG images into a single Markdown file using Apple Vision, tesseract, or MLX OCR
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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 | image-ocr |
| description | Extract text from a directory of JPG/JPEG images into a single Markdown file using Apple Vision, tesseract, or MLX OCR |
Batch-processes a directory of JPG/JPEG images and produces a combined Markdown file containing the OCR text from each image.
Use this skill when the user wants to extract text from one or more JPG/JPEG images -- for example scanned documents, photos of whiteboards, or screenshots.
pyobjc-core and pyobjc-framework-Vision.tesseract CLI binary. Cross-platform. Optional preprocessing (grayscale, autocontrast, upscale) via Pillow.mlx-ocr.python3 ${CLAUDE_PLUGIN_ROOT}/scripts/ocr_jpgs_to_markdown.py \
--input <directory-of-jpgs> \
--output <output.md> \
[--backend auto|vision|tesseract|mlx_ocr] \
[--start N] [--limit N] \
[--languages en-US] \
[--fast] \
[--workers N] \
[--tesseract-lang eng] [--tesseract-psm 6] [--tesseract-oem 1] \
[--preprocess] [--preprocess-scale 2.0] \
[--det-lang eng] [--rec-lang lat] \
[--mlx-worker-python /path/to/python3.11]
| Argument | Default | Description |
|---|---|---|
--input | ezgif-... | Directory containing .jpg/.jpeg files |
--output | training_plan_ocr.md | Output Markdown file path |
--backend | auto | OCR backend: auto, vision, tesseract, mlx_ocr |
--start | 0 | Start index (0-based) within sorted file list |
--limit | None | Max files to process |
--languages | en-US | Comma-separated recognition languages (Vision) |
--fast | false | Use faster, less accurate recognition level (Vision) |
--workers | CPU count | Number of parallel workers |
--preprocess | false | Grayscale/autocontrast/upscale before tesseract |
--preprocess-scale | 2.0 | Upscale factor for preprocessing |