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Paper Bilingual

v1.2.0

将PDF论文转换为双语Markdown。对用户提供PDF URL或本地PDF文件，提取文本转为Markdown，保留图片，逐段插入中文翻译。用于学术论文研读、翻译对比。

⭐ 0· 79·0 current·0 all-time
byCiel@caottt
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Benign
high confidence
✓
Purpose & Capability
The name/description (PDF -> bilingual Markdown, preserve images) matches the SKILL.md workflow: download (if URL), extract text and images with PyMuPDF, crop figures, then translate paragraphs with the current LLM and write Markdown. All requested operations are expected for this use case.
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Instruction Scope
Instructions are specific and limited to the task (downloading the supplied URL, parsing PDF, saving images and Markdown, translating per-paragraph). Minor scope notes: the download example uses PowerShell (Windows-specific) while the skill declares no OS restriction; translation is done via the current session LLM which may hit token limits for large PDFs.
✓
Install Mechanism
This is instruction-only with no install spec (lowest install risk). It lists dependencies (Python 3.8+, PyMuPDF, Pillow) but does not provide an automated install step; users will need to pip-install them themselves.
✓
Credentials
No environment variables, credentials, or config paths are requested. The only external network action is downloading the user-provided PDF URL, which is consistent with the stated purpose.
✓
Persistence & Privilege
always is false and the skill does not request elevated or persistent privileges. It writes files under a per-paper folder (memory/paper-bilingual/...), which is reasonable for its function.
Assessment
This skill appears coherent for converting PDFs to bilingual Markdown. Before using it: ensure you trust the PDF source (the skill will download the provided URL), have Python 3.8+ and install pymupdf and pillow with pip, and be aware it will write files under memory/paper-bilingual/ on your filesystem. The download example uses PowerShell (Windows); adapt the download step on macOS/Linux. For very long papers, per-paragraph translation via the LLM may hit token or cost limits — consider splitting work or translating selectively. No credentials or hidden network endpoints are requested by the skill.

Like a lobster shell, security has layers — review code before you run it.

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License

MIT-0
Free to use, modify, and redistribute. No attribution required.
Termshttps://spdx.org/licenses/MIT-0.html

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