PPT 视觉增强工具,支持多种风格渲染、交互式播放器生成和视频合成。可与 ppt-generator Skill 协同工作,实现从内容规划到视觉呈现的完整流程。
npx skills add https://github.com/anbeime/skill --skill nanobanana-ppt-visualizer
pillow>=9.0.0
python-dotenv>=0.19.0
# Ubuntu/Debian
sudo apt-get install ffmpeg
# macOS
brew install ffmpeg
assets/styles/ 目录,列出可用风格:gradient-glass.md:渐变毛玻璃风格(科技感、商务)vector-illustration.md:矢量插画风格(温暖、教育)scripts/video_materials.py 管理视频素材scripts/generate_viewer.py 生成 HTML 播放器:scripts/video_composer.py:当用户直接提供 PPT 内容(JSON 格式)时:
用户请求:"生成一个关于 AI 产品的 PPT"
┌─────────────────────────────────────────────────────────┐
│ ppt-generator Skill │
├─────────────────────────────────────────────────────────┤
│ 1. 主题分析师:分析主题,生成大纲 │
│ 2. 模板设计师:推荐布局 │
│ 3. 内容策划师:规划内容结构 │
│ 4. 文本创作者:撰写内容 │
│ 5. 视觉设计师:提供配图建议 │
│ 6. 优化编辑师:优化文本 │
│ 7. PPT 构建师:生成 JSON 数据 │
└─────────────────────────────────────────────────────────┘
│
▼ 输出 JSON
┌─────────────────────────────────────────────────────────┐
│ nanobanana-ppt-visualizer Skill │
├─────────────────────────────────────────────────────────┤
│ 1. 接收 JSON 数据 │
│ 2. 选择视觉风格 │
│ 3. 生成图片(使用智能体能力) │
│ 4. 生成 HTML 播放器 │
│ 5. 可选:视频合成 │
└─────────────────────────────────────────────────────────┘
│
▼ 输出
交互式播放器 + 完整视频(可选)
ppt-generator 输出的 JSON 格式与 nanobanana-ppt-visualizer 完全兼容:
{
"metadata": {
"title": "演示文稿标题",
"author": "作者姓名"
},
"slides": [
{
"layout": "TitleSlide",
"title": "封面标题",
"content": ["副标题"],
"notes": "备注"
}
]
}
python scripts/generate_viewer.py --input ./ppt_data.json --style gradient-glasspython scripts/video_composer.py --output ./full_ppt_video.mp4Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take anbeime/nanobanana-ppt-visualizer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference brew, apt.
Without those the skill loads but fails at the first command.