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norman-paper2ppt

Turn academic paper PDFs into Chinese research presentations with original figures, optional speaker notes and editable text/image PPTX. Includes embedded helpers and Fig2PPT for Windows, macOS and Linux.

Published by N-tongxue ↗N-tongxue/norman-skill

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SKILL.md

name: norman-paper2pptdescription: Turn academic paper PDFs into Chinese research presentations with original figures, optional speaker notes and editable text/image PPTX. Includes embedded helpers and Fig2PPT for Windows, macOS and Linux.metadata:   version: "1.1.0"

Norman Paper2PPT

Derive <skill-root> from this SKILL.md location. Select the executable matching the actual host OS and CPU. Before reading a paper or producing slides, run its guide command and follow the complete embedded workflow. Load the required references using read <resource>; list shows logical resource names. The guide confirms missing presentation mode, speaker notes and editable output one question at a time, reusing choices already supplied. All full instructions, references, attribution, helpers, Fig2PPT and runtimes are embedded. Do not search for loose scripts or install Python.

Host	Executable
Windows 10+ x64	norman-paper2ppt.exe
macOS 14+ Apple Silicon	norman-paper2ppt-macos-arm64
macOS 14+ Intel	norman-paper2ppt-macos-x64
Linux glibc 2.17+ x64	norman-paper2ppt-linux-x64
Linux glibc 2.17+ ARM64	norman-paper2ppt-linux-arm64

Windows PowerShell:

& "<skill-root>/norman-paper2ppt.exe" guide

Mac/Linux: select using the real OS and uname -m. On Apple Silicon under Rosetta, sysctl -n hw.optional.arm64 returning 1 identifies Apple Silicon. Example for Apple Silicon:

chmod u+x "<skill-root>/norman-paper2ppt-macos-arm64"
"<skill-root>/norman-paper2ppt-macos-arm64" guide

The host AI client interprets the paper and generates layouts with its image tool. No AI model is bundled. Optional editable output uses fig2ppt <command> in the same executable and Quick text/images only; load read vendor/norman-fig2ppt/guide.md when selected. Windows assembly/rendering requires local PowerPoint; Mac/Linux require a verified presentation backend and renderer. No separate Python installation or first-run download is needed.