Convert scattered source materials into structured outlines and final slides.
npx skills add https://github.com/wilingna/ai-ppt-toolkit --skill ai-ppt-toolkit
Please help me install this Agent Skill. Skill: ai-ppt-toolkit Source: https://github.com/wilingna/ai-ppt-toolkit/tree/main Command: npx skills add https://github.com/wilingna/ai-ppt-toolkit --skill ai-ppt-toolkit
This Skill addresses the challenge of turning scattered, messy source material into a coherent, delivery-ready PPT. It guides users through a three-step workflow that converts raw inputs into a structured outline, polished content, and final slides, reducing time wasted on manual synthesis and formatting.
To get started, simply initiate the skill when you mention doing a PPT, a presentation, or a '三件套' workflow, and provide the raw materials; follow the prompts to upload or paste sources, then proceed through NotebookLM, Gemini, and Gamma in order.
High-intent search queries and answers about installing and using this skill.
To turn scattered materials into a presentation, compress raw inputs into a storytelling arc using NotebookLM, elevate language with Gemini, and generate final visuals with Gamma. This structured workflow ensures coherent, delivery-ready slides for management briefs.
The best way to create a management briefing is applying an AI workflow that synthesizes HR data and financial figures into a strategic proposal. It transforms unstructured inputs into a clear, decision-ready presentation through automated structuring and polishing.
Yes, you need NotebookLM, Gemini, and Gamma to execute this workflow. The process requires these three tools in sequence to design the structure, upgrade the expression, and generate the final slides for your presentation.
Yes, you can use Gamma to convert a Markdown outline into ready-to-deliver slides. In this workflow, Gamma handles the visual conversion stage after NotebookLM structures the data and Gemini upgrades the business-ready framing.
The AI PPT workflow handles messy sources by compressing input data into a storytelling arc via NotebookLM. It then applies explicit prompts and Markdown structures to feed into Gemini for language elevation and Gamma for visual generation.
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