Study img

Skill 2362094903-ops/study-assistant-skills/study-img

AI 学习辅导 · Claude Code Skill 套件：根据上传的教材/课件按章节学习，适用于考研、期末、资格考试——思维导图 / 讲义 / 真题风格试卷 / 错题本 / 费曼检验 / 掌握度仪表盘，进度跨会话保存

Install

npx -y skills add 2362094903-ops/study-assistant-skills --skill study-img

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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.

SKILL.md

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Study Image Reading

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 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.