Use when a Xiaohongshu post, article, work report, teaching material, or chart needs a material-style center explanation image, mechanism diagram, flow diagram, chart beautification, or reusable visual material with readable Chinese labels.
npx skills add https://github.com/jackbauerxu/workbuddy-xhs-skills --skill wb-xhs-material-illustration
先让图片讲清一个已确认的概念、机制、流程、关系或数据结论,再决定是否导出其中可复用的物件、箭头和栏目组件。图片不是无字装饰,也不是完整小红书卡片;它是能放进卡片、PPT、文章或文档的中心解释图。
#002FA7。先让关系可读,再增加可复用素材。Required chart accuracy,逐项写明刻度、类别、数值、单位、误差范围和“不可新增/交换类别”的约束。在生成前先记录 art_direction:本图要讲清的唯一关系、读者的阅读路径、对象层级、材质/空间语言和强调色的职责。材质化不等于把概念换成一排装饰物;画面必须在不读长文时也能说明关系。
候选必须通过结构可读、标签准确、材质完成度和视觉层级:
结构可读:箭头、循环、层级或对比的方向一眼成立;图表还要保留所有已确认不变量。标签准确:图内短标签逐字正确,贴在对应对象或关系旁,缩小后仍能辨认。材质完成度:暖白底、黑墨线、浅灰材质对象与单一强调色形成有目的的空间、明暗和边缘层次,不出现漂浮、断裂或互相遮挡的对象。视觉层级:中心关系最先被看见,强调色只引导关键路径;不能把通用 3D 图标拼盘、无关装饰或完整卡片外框误当成解释图。第一次生成是候选而不是交付。quality_gate 未通过时,记录失败的结构、标签、材质或层级维度,只修该维度;通过后才记录为可交付资产。
explanation_type:解释图 / 流程 / 循环 / 对比 / 层级 / 场景 / 图表。one_sentence:本图说明的唯一关系或结论。confirmed_labels:实际放入图内的短标签及其事实来源。chart_invariants:图表任务必须保留的类型、数据、坐标、单位和结论;非图表则为 not_applicable。reference_cues:冷门实体、品牌、科学装置或历史物件的稳定视觉线索;参考只补事实,不借用外部图片排版或风格。reusable_materials:可从中心图拆出的物件、箭头、角标或组件。op7418/guizang-material-illustration 吸收“材质化解释图、图表语义重画、参考只补事实、中心图与外层卡片分工”的方法,不复制其图片、成套素材、提示词或模板。Create 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 jackbauerxu/wb-xhs-material-illustration 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.