Generate hand-drawn 16:9 article illustrations with the Grav character IP, sparse annotations, and absurd but clear visual metaphors.
npx skills add https://github.com/lingxling/awesome-skills-cn --skill article-illustrations
Generate 16:9 landscape hand-drawn illustrations for articles, blog posts, and technical content. Each illustration captures one cognitive anchor point from an article and turns it into a clean, absurd, memorable whiteboard-sketch explanation.
The skill uses a recurring character IP called Grav: a small, round, always-floating figure with dot eyes and a thin antenna. Grav participates in the core action of every illustration — never just decoration.
Repository: vipin-si/article-illustrations
Read the article and identify cognitive anchor points — core judgments, turning points, input/output loops, before/after contrasts, and common pitfalls. Don't distribute illustrations evenly; prioritize moments that benefit from visual explanation.
For each illustration, define:
Use the generate_image tool with the built-in prompt template. Each image follows strict style rules:
Verify each image against the QA checklist: correct format, Grav present and active, original metaphor, clean composition, sparse annotations, correct color usage.
Analyze this article and create a shot list of 5 illustrations.
Don't generate images yet — just plan which cognitive anchor points
deserve illustrations and what each image should convey.
<paste article>
Generate 4 Grav-style illustrations for this article.
Requirements: 16:9 landscape, pure white background, black hand-drawn
line art, sparse red/orange/blue English annotations.
<paste article>
Generate one 16:9 illustration for this concept:
"Trust isn't declared — it's built one piece of evidence at a time."
Grav must perform the core action. Maximum 5 annotation labels.
This illustration is on the right track, but Grav feels like decoration.
Keep the core meaning but regenerate: make Grav the one actually
driving the structure.
| Element | Rule |
|:--------|:-----|
| Background | Pure white — no cream, texture, gradients, or shadows |
| Line art | Black, hand-drawn, slightly wobbly, not mechanical |
| Whitespace | Main subject 40–60% of canvas, 35%+ empty space |
| Annotations | Handwritten English, 2–5 words each, max 5–8 per image |
| Color: Black | Main line art, characters, structures, objects |
| Color: Red | Key highlights, problems, warnings, results |
| Color: Orange | Main flow, paths, arrows, direction |
| Color: Blue | Supplementary notes, feedback, system state |
| Prohibited | Green, purple, yellow, pink, gradients, drop shadows, 3D, realistic UI |
Solution: Remove 30% of elements, increase whitespace, make it weirder
Solution: Redesign so Grav IS the mechanism — becomes the funnel, dangles from the lever, is suspended inside the machine
Solution: Replace the physical object entirely — same concept, different analogy
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 lingxling/article-illustrations 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.