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Multimodal document deep analysis tool based on Zhipu GLM-OCR, GLM-4.7, and GLM-4.6V.
Passed 21 of 22 test cases with the skill, and 5 of 22 without it.
and 19 more in the full report
17 fixed5 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
If a multi-page document at `/data/annual_report.pdf` is passed into `scripts/glm_ocr_pipeline.py` without script modifications, which pages are processed by default?
| Checked | Without skill | With skill |
|---|---|---|
| States that only the first page of the PDF is processed by default. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
This Skill consists of two core script stages, orchestrated through glm_ocr_pipeline.py:
scripts/glm_ocr_extract.py)scripts/glm_understanding.py)# 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"
| 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) |
test cases passed, vs 5 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.