Study Image Reading

SkillDocs & knowledge

Image-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.

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

ScenarioModeRequired output
Scanned textbook page / photographed paper / handout--mode ocrStructured 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 figureTeaching-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 answerVerbatim transcription; preserve errors; LaTeX formulas; use 【?】 for illegible characters.
Unsureno modeComprehensive recognition.

Workflow hookups

  • Scanned PDFs: render flagged pages with extract_pdf.py --render-scanned, recognize, and merge into internal/textbook/chapter-XX.md.
  • Image-heavy PPT slides: export with extract_pptx.py --render-images, recognize, and merge into internal/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 with source_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