Generate pixel art SVG illustrations for READMEs, docs, or slides. Use when user says "画像素图", "pixel art", "make an SVG illustration", "README hero image", or wants a cute visual.
npx skills add https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep --skill pixel-art
Create a pixel art SVG illustration: $ARGUMENTS
<rect> with width/height of 7px<g transform="translate(x,y)"> to position and reuse character groupsKeep it simple — 3-5 colors per character:
#FFDAB9 (light), #E8967A / #D4956A (blush/shadow)#333#8B5E3C (brown), #2C2C2C (black), #FFD700 (blonde), #C0392B (red)#4A9EDA for blue, #74AA63 for green)#444#555 (glasses frames), #FFD700 (crown)Row 0 (hair top): 4 pixels centered
Row 1 (hair): 6 pixels wide
Row 2 (face top): 6 pixels — all skin
Row 3 (eyes): 6 pixels — skin, eye, skin, skin, eye, skin
Row 4 (mouth): 6 pixels — skin, skin, mouth, mouth, skin, skin
Row 5 (body top): 8 pixels — hand, 6 shirt, hand
Row 6 (body): 6 pixels — all shirt
Row 7 (legs): 2+2 pixels — with gap in middle
orient="auto" markers for arrow heads<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 W H" font-family="monospace">
<defs>
<!-- Arrow markers if needed -->
</defs>
<rect width="W" height="H" fill="#fafbfc" rx="12"/> <!-- Background -->
<!-- Characters via <g transform="translate(...)"> -->
<!-- Dialogue bubbles: <rect> + <polygon> tail + <text> -->
<!-- Arrows: <line> with marker-end -->
<!-- Labels: <text> with text-anchor="middle" -->
</svg>
<!-- Blue bubble (left character speaks) -->
<rect x="110" y="29" width="280" height="26" fill="#e8f4fd" stroke="#4a9eda" stroke-width="1.5" rx="8"/>
<!-- Tail pointing left toward character -->
<polygon points="108,41 99,47 108,46" fill="#e8f4fd" stroke="#4a9eda" stroke-width="1.5"/>
<rect x="107" y="40" width="3" height="7" fill="#e8f4fd"/> <!-- covers stroke at junction -->
<text x="123" y="46" font-size="13px">📄 Message here</text>
<!-- Orange bubble (right character responds) -->
<rect x="490" y="71" width="280" height="26" fill="#fdf2e8" stroke="#da8a4a" stroke-width="1.5" rx="8"/>
<!-- Tail pointing right toward character -->
<polygon points="772,83 781,89 772,88" fill="#fdf2e8" stroke="#da8a4a" stroke-width="1.5"/>
<rect x="770" y="82" width="3" height="7" fill="#fdf2e8"/>
<text x="503" y="88" font-size="13px">🤔 Response here</text>
<defs>
<marker id="ar" markerWidth="8" markerHeight="6" refX="8" refY="3" orient="auto">
<polygon points="0 0, 8 3, 0 6" fill="#4a9eda"/>
</marker>
</defs>
<!-- Right arrow (→): x1 < x2 -->
<line x1="392" y1="42" x2="465" y2="42" stroke="#4a9eda" stroke-width="2" marker-end="url(#ar)"/>
<!-- Left arrow (←): x1 > x2 -->
<line x1="488" y1="84" x2="420" y2="84" stroke="#da8a4a" stroke-width="2" marker-end="url(#ar-o)"/>
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Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
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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 wanshuiyin/pixel-art 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.