Indexé dans Registry

study-img

Prix non confirmé★ 22 Stars GitHubRegistre mis à jour · 14 sept. 2026agent-skill

Vue d’ensemble

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.

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

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.

Voir la source ↗

Métadonnées du fichier

name: study-img
description: >
  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.

Voir le texte original

---
name: study-img
description: >
  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.
---

# 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

```bash
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.

Examiner la source

Prix et coûts d’utilisation

Obtenir le skill: Prix non confirmé
L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: MIT
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
The script does not validate that input files are actual images before sending them to the API, which could cause unexpected errors or unnecessary API calls.
No explicit handling for very large image files, which might exceed API limits or cause memory issues.
Low GitHub adoption signal
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

RépertoriéExaminé par IA

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: 2362094903-ops/study-assistant-skills
Licence: MIT
Version: Unknown
Dernier push GitHub: 8 août 2026
Registre mis à jour: 14 sept. 2026
Chemin des instructions: study-img/SKILL.md @ 3f555b845ac1

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

54/100

Revue nécessaire

Confiance

51/100

Do not auto-install

Audit

67/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

Dependency or permission surface needs review
Permission surface may require sandboxing
The script does not validate that input files are actual images before sending them to the API, which could cause unexpected errors or unnecessary API calls.
No explicit handling for very large image files, which might exceed API limits or cause memory issues.
Low GitHub adoption signal
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 22 GitHub stars
Stars/forks activity: 22 stars, 1 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

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Ouvrir l’API d’installation Format texte LLM Ouvrir Manifest Plan de résolution auto

Plus de détails

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Créateur: 2362094903-ops
Source: 2362094903-ops/study-assistant-skills
Indexé par: Index communautaire OpenAgentSkill

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/2362094903-ops-study-img?metric=listed&label=Listed)](https://www.openagentskill.com/skills/2362094903-ops-study-img?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/2362094903-ops-study-img?metric=trust&label=Trust)](https://www.openagentskill.com/skills/2362094903-ops-study-img?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/2362094903-ops-study-img?metric=audit&label=Audit)](https://www.openagentskill.com/skills/2362094903-ops-study-img/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/2362094903-ops-study-img?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/2362094903-ops-study-img?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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