berabuddies
Indexado en Registry
pdf-ocr-layout
Multimodal document deep analysis tool based on Zhipu GLM-OCR, GLM-4.7, and GLM-4.6V.
Resumen
Multimodal document deep analysis tool based on Zhipu GLM-OCR, GLM-4.7, and GLM-4.6V.
Leer documentación completa
Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.
GLM-OCR Multimodal Deep Analysis
This tool builds a high-precision document parsing pipeline: using GLM-OCR for layout element extraction, calling GLM-4.7 for logical interpretation of table data, and calling GLM-4.6V for multimodal visual interpretation of images and charts.
Pipeline Implementation Architecture
This Skill consists of two core script stages, orchestrated through glm_ocr_pipeline.py:
1. Extraction Stage (scripts/glm_ocr_extract.py)
- Core Model: GLM-OCR
- Function: Responsible for physical layout analysis of documents
- Output: Extract table HTML and clean to Markdown, automatically crop independent chart image files based on Bbox coordinates, and generate intermediate JSON containing full page reading order
2. Understanding Stage (scripts/glm_understanding.py)
- Core Model: GLM-4.7 (text) / GLM-4.6V (visual)
- Function: Responsible for deep semantic reasoning of content
- Logic:
- Tables: Combine full text context, use GLM-4.7 to analyze business meaning of Markdown table data
- Charts: Combine full text context + cropped images, use GLM-4.6V for multimodal visual analysis
Invocation Methods
Command Line Invocation
# Run complete pipeline: extraction -> cropping -> understanding analysis, supports input in .pdf, .jpg, .png and other formats
python scripts/glm_ocr_pipeline.py \
--file_path "/data/report_page.jpg" \
--output_dir "/data/output"
API Parameter Description
| Parameter | Type | Required | Description |
|---|---|---|---|
| file_path | string | ✅ | Absolute path to input file (supports .pdf, .png, .jpg) |
| output_dir | string | ✅ | Result output directory (used to save cropped images and JSON reports) |
Return Result Structure (JSON)
The tool returns a list containing layout elements and their deep understanding:
[
{
"type": "table",
"bbox": [100, 200, 500, 600],
"content_info": "| Revenue | Q1 |\n|---|---|\n| 100M | ... |",
"deep_understanding": "(Generated by GLM-4.7) This table shows Q1 2024 revenue data. Combined with the 'market expansion strategy' mentioned in paragraph 3 of the body text, it can be seen that..."
},
{
"type": "image",
"bbox": [100, 700, 500, 900],
"content_info": "/data/output/images/report_page_img_2.png",
"deep_understanding": "(Generated by GLM-4.6V) This is a system architecture diagram. Visually, it shows the flow of clients connecting to servers through a Load Balancer. Combined with the title 'Fig 3' and context, this diagram is mainly used to illustrate..."
}
]
Environment Requirements
- Environment variable
ZHIPU_API_KEYmust be configured - Python 3.8+
- Dependencies:
zhipuai,pillow,beautifulsoup4
Notes
1. Model Routing Strategy
- Table (表格): Content passed to GLM-4.7, combined with full text Markdown context for logical reasoning
- Image (图片): Image Base64 encoded and passed to GLM-4.6V, combined with OCR-extracted titles and full text context for multimodal understanding
2. Context Association
All understanding is based on the complete layout logic of the document (Markdown Context), not isolated fragment analysis.
3. PDF Processing
Multi-page PDFs default to processing the first page. For batch processing, please extend the loop logic at the script level.
Metadatos del archivo
name: pdf-ocr-layout description: Multimodal document deep analysis tool based on Zhipu GLM-OCR, GLM-4.7, and GLM-4.6V. Use when: - Need to extract tables from documents (PDF/images) with high precision and convert to Markdown format - Need to automatically crop and extract illustrations and charts from document pages as independent files - Need to perform deep semantic understanding on extracted charts (based on GLM-4.6V visual analysis) - Need to perform logical analysis on extracted table data (based on GLM-4.7 text analysis) Core Architecture: 1. Visual Extraction: GLM-OCR 2. Semantic Understanding: GLM-4.7 (text/tables) + GLM-4.6V (multimodal/images)
Ver texto original
---
name: pdf-ocr-layout
description: Multimodal document deep analysis tool based on Zhipu GLM-OCR, GLM-4.7, and GLM-4.6V.
Use when:
- Need to extract tables from documents (PDF/images) with high precision and convert to Markdown format
- Need to automatically crop and extract illustrations and charts from document pages as independent files
- Need to perform deep semantic understanding on extracted charts (based on GLM-4.6V visual analysis)
- Need to perform logical analysis on extracted table data (based on GLM-4.7 text analysis)
Core Architecture:
1. Visual Extraction: GLM-OCR
2. Semantic Understanding: GLM-4.7 (text/tables) + GLM-4.6V (multimodal/images)
---
# GLM-OCR Multimodal Deep Analysis
This tool builds a high-precision document parsing pipeline: using **GLM-OCR** for layout element extraction, calling **GLM-4.7** for logical interpretation of table data, and calling **GLM-4.6V** for multimodal visual interpretation of images and charts.
## Pipeline Implementation Architecture
This Skill consists of two core script stages, orchestrated through `glm_ocr_pipeline.py`:
### 1. Extraction Stage (`scripts/glm_ocr_extract.py`)
- **Core Model**: GLM-OCR
- **Function**: Responsible for physical layout analysis of documents
- **Output**: Extract table HTML and clean to Markdown, automatically crop independent chart image files based on Bbox coordinates, and generate intermediate JSON containing full page reading order
### 2. Understanding Stage (`scripts/glm_understanding.py`)
- **Core Model**: GLM-4.7 (text) / GLM-4.6V (visual)
- **Function**: Responsible for deep semantic reasoning of content
- **Logic**:
- **Tables**: Combine full text context, use GLM-4.7 to analyze business meaning of Markdown table data
- **Charts**: Combine full text context + cropped images, use GLM-4.6V for multimodal visual analysis
## Invocation Methods
### Command Line Invocation
```bash
# Run complete pipeline: extraction -> cropping -> understanding analysis, supports input in .pdf, .jpg, .png and other formats
python scripts/glm_ocr_pipeline.py \
--file_path "/data/report_page.jpg" \
--output_dir "/data/output"
```
## API Parameter Description
| Parameter | Type | Required | Description |
| --- | --- | --- | --- |
| file_path | string | ✅ | Absolute path to input file (supports .pdf, .png, .jpg) |
| output_dir | string | ✅ | Result output directory (used to save cropped images and JSON reports) |
## Return Result Structure (JSON)
The tool returns a list containing layout elements and their deep understanding:
```json
[
{
"type": "table",
"bbox": [100, 200, 500, 600],
"content_info": "| Revenue | Q1 |\n|---|---|\n| 100M | ... |",
"deep_understanding": "(Generated by GLM-4.7) This table shows Q1 2024 revenue data. Combined with the 'market expansion strategy' mentioned in paragraph 3 of the body text, it can be seen that..."
},
{
"type": "image",
"bbox": [100, 700, 500, 900],
"content_info": "/data/output/images/report_page_img_2.png",
"deep_understanding": "(Generated by GLM-4.6V) This is a system architecture diagram. Visually, it shows the flow of clients connecting to servers through a Load Balancer. Combined with the title 'Fig 3' and context, this diagram is mainly used to illustrate..."
}
]
```
## Environment Requirements
- Environment variable `ZHIPU_API_KEY` must be configured
- Python 3.8+
- Dependencies: `zhipuai`, `pillow`, `beautifulsoup4`
## Notes
### 1. Model Routing Strategy
- **Table (表格)**: Content passed to **GLM-4.7**, combined with full text Markdown context for logical reasoning
- **Image (图片)**: Image Base64 encoded and passed to **GLM-4.6V**, combined with OCR-extracted titles and full text context for multimodal understanding
### 2. Context Association
All understanding is based on the complete layout logic of the document (Markdown Context), not isolated fragment analysis.
### 3. PDF Processing
Multi-page PDFs default to processing the first page. For batch processing, please extend the loop logic at the script level.
Revisar el código fuente
Precio y costes de ejecución
- Obtener el skill
- Precio sin confirmar
- Ejecutarlo
- Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
- Licencia
- Apache-2.0
- Precio sin confirmar
- No hemos confirmado el precio. Los enlaces existentes al código y a la instalación siguen disponibles.
Obtener gratis no significa ejecutar gratis. El precio no es una evaluación de seguridad. Enviar información de precio →
Fuente del skill registrada
La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.
Revisar antes de instalar: Evitar instalación automática
Licencia: Apache-2.0
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- SKILL.md references `scripts/` directory but actual files are in `script/` (singular).
- Dependency `zai` is imported in code but not listed in SKILL.md dependencies (which mention `zhipuai`).
- Typo in `glm_understanding.py`: `thinking={"type": "diabled"}` should be `"disabled"`.
- Multi-page PDFs are limited to first page; no batch processing support is provided.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.
Empieza con una tarea pequeña
- 1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
- 2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
- 3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.
Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.
Fuente y notas de uso
Indexado
Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.
- Repositorio fuente
- berabuddies/Semia
- Licencia
- Apache-2.0
- Versión
- 1.0.0
- Último push de GitHub
- 1 sept 2026
- Registro actualizado
- 9 oct 2026
- Ruta de instrucciones
- tests/fixtures/skills/baokui/pdf-ocr-layout/SKILL.md @ 379bc25fe998
Versión declarada en el registro; consulta las versiones de la fuente.
Calidad
68/100
Prometedor
Confianza
56/100
Do not auto-install
Auditoría
73/100
Requiere revisión
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- SKILL.md references `scripts/` directory but actual files are in `script/` (singular).
- Dependency `zai` is imported in code but not listed in SKILL.md dependencies (which mention `zhipuai`).
- Typo in `glm_understanding.py`: `thinking={"type": "diabled"}` should be `"disabled"`.
- Multi-page PDFs are limited to first page; no batch processing support is provided.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Resultados
- —
Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.
Acceso para agentes
La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.
Más detalles
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"install": "npx skills add berabuddies/Semia --skill pdf-ocr-layout",
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"permissionSurface": "secrets or environment access, shell or command execution",
"documentation": "Usable metadata, review docs",
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"Dependency `zai` is imported in code but not listed in SKILL.md dependencies (which mention `zhipuai`).",
"Typo in `glm_understanding.py`: `thinking={\"type\": \"diabled\"}` should be `\"disabled\"`.",
"Multi-page PDFs are limited to first page; no batch processing support is provided.",
"Financial research output is not financial advice; require human review before any live investment decision."
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"install": "https://www.openagentskill.com/api/skills/berabuddies-pdf-ocr-layout/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/berabuddies-pdf-ocr-layout"
}
}Para el creador
Fuente de la ficha
Indexado por Registry
Esta ficha se indexó desde fuentes públicas y no está marcada como oficial hasta que se apruebe una reclamación de mantenedor.
- Creador
- berabuddies
- Fuente
- berabuddies/Semia
- Indexado por
- Índice comunitario de OpenAgentSkill
La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.
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Esta ficha Indexado por Registry se atribuye a berabuddies, pero aún no está marcada como oficial. Reclámala para añadir una señal de propietario verificado y hacer más fiables futuras actualizaciones de lanzamiento, instalación y auditoría.
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Kit de enlaces para creadores
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[](https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout/audit)
[](https://www.openagentskill.com/skills/berabuddies-pdf-ocr-layout?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)Señal de comunidad
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