Study Image Reading
SkillDocs & knowledgeImage-reading sub-skill (orchestrated by study-assistant; also usable standalone). Use for ANY study material that must be visually inspected: scanned textbook pages, courseware figures/charts/diagrams, photographed exam papers, photos of handwritten answers or notes, and png/jpg/jpeg/gif/webp/bmp files. Supports OCR, teaching-grade figure descriptions, and verbatim handwritten-answer transcription.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Study Image Reading skill
What this skill tells your AI
The instructions your AI receives, as published by 2362094903-ops/study-assistant-skills in study-img/SKILL.md and read by ahel’s review.
Output language: ALL learner-facing content MUST be Simplified Chinese.
Try native vision first
Use the available image-reading capability directly when possible.
- If you can see the image, produce the required mode output below.
- If image reading fails or the model has no vision, use the external vision API script.
One failed native attempt per session is enough evidence; do not retry every image.
External vision API
python3 ~/.claude/skills/study-img/scripts/recognize.py <image> --mode <mode>
First-use configuration: ask for provider type, base URL/API key, and vision model. Store config in ~/.config/study-img/config.json, chmod 600, and never repeat the full API key in conversation.
Modes
| Scenario | Mode | Required output |
|---|---|---|
| Scanned textbook page / photographed paper / handout | --mode ocr | Structured Markdown transcription; formulas as LaTeX; figures as [图:...] placeholders with enough detail to locate them. |
| Textbook/courseware figure, coordinate plot, table image, flowchart, chart | --mode figure | Teaching-grade description complete enough to redraw or convert into a lecture figure/table. Include axes, labels, variables, trends, data rows, and the conclusion. |
| Learner handwritten answers | --mode answer | Verbatim transcription; preserve errors; LaTeX formulas; use 【?】 for illegible characters. |
| Unsure | no mode | Comprehensive recognition. |
Workflow hookups
- Scanned PDFs: render flagged pages with
extract_pdf.py --render-scanned, recognize, and merge intointernal/textbook/chapter-XX.md. - Image-heavy PPT slides: export with
extract_pptx.py --render-images, recognize, and merge intointernal/textbook/chapter-XX.md. - Lecture figures: when a
[图:...], chart, curve, or table is important for understanding, recognize it with--mode figure; then study-teach must include the useful visual/table/formula in the lecture JSON withsource_ref. - Handwritten answer grading: transcribe with
--mode answer, show uncertain parts to the learner, then hand to study-quiz for grading.
Caveats
Vision output can misread formulas and numbers. Cross-check against surrounding text, dimensions, and internal consistency before teaching or grading from it. If a figure/table remains doubtful, say so and ask the learner to confirm from the original.
Signals
- GitHub stars
- 21
- Forks
- 1
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
study-img- Source
- github.com/2362094903-ops/study-assistant-skills